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Title:
Developing the nomological network of perceived corporate affinity for technology : a three essay dissertation
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English
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Fleming, David
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University of South Florida
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Sales
Services
Technology
Congruity
Self-directed learning
Dissertations, Academic -- Marketing -- Doctoral -- USF   ( lcsh )
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non-fiction   ( marcgt )

Notes

Abstract:
ABSTRACT: Technology is changing the face of both the sales and service domains. Honebein and Cammarano (2006) note that properly implemented self-service technologies serve dual purposes of decreasing firm overhead costs, while simultaneously engaging the customer in a way encourages the co-create of value for both parties. To get these benefits stakeholders must be willing to adopt and use the technologies that are available. Traditionally, this has lead to the research question "How do firms do this?" However, according to a recent article by Woodall, Colby and Parasuraman (2007), consumers are now demanding more technology-based options and becoming more technologically savvy. This changes the research focus to answering the question "How can firms be seen as able to deliver technology-based options effectively, efficiently and securely to meet the demands of this new "e-service" model?" The purpose of this dissertation is to examine the role of stakeholder perceptions of firm attitudes toward technology in answering this question. Perceived corporate affinity for technology (Fleming and Artis forthcoming) is a measure stakeholder perception of a firm's general positive affect toward technology, and was developed and validated in sales and services contexts using samples of both employees and customers. The studies of this dissertation test potential antecedents, consequence and boundary conditions of stakeholder perceptions of corporate affinity for technology in three key groups, namely managers, employees and customers. To accomplish this purpose, the following research questions, one for each key group of stakeholders, were proposed for this study:
Thesis:
Dissertation (Ph.D.)--University of South Florida, 2010.
Bibliography:
Includes bibliographical references.
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by David Fleming.
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Title from PDF of title page.
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Document formatted into pages; contains X pages.
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Includes vita.

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ABSTRACT: Technology is changing the face of both the sales and service domains. Honebein and Cammarano (2006) note that properly implemented self-service technologies serve dual purposes of decreasing firm overhead costs, while simultaneously engaging the customer in a way encourages the co-create of value for both parties. To get these benefits stakeholders must be willing to adopt and use the technologies that are available. Traditionally, this has lead to the research question "How do firms do this?" However, according to a recent article by Woodall, Colby and Parasuraman (2007), consumers are now demanding more technology-based options and becoming more technologically savvy. This changes the research focus to answering the question "How can firms be seen as able to deliver technology-based options effectively, efficiently and securely to meet the demands of this new "e-service" model?" The purpose of this dissertation is to examine the role of stakeholder perceptions of firm attitudes toward technology in answering this question. Perceived corporate affinity for technology (Fleming and Artis forthcoming) is a measure stakeholder perception of a firm's general positive affect toward technology, and was developed and validated in sales and services contexts using samples of both employees and customers. The studies of this dissertation test potential antecedents, consequence and boundary conditions of stakeholder perceptions of corporate affinity for technology in three key groups, namely managers, employees and customers. To accomplish this purpose, the following research questions, one for each key group of stakeholders, were proposed for this study:
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Developing the Nomological Network of Perceived Corporate Affinity f or Technology: A Three Essay Dissertation by David Earl Fleming A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy Department of Marketing College of Business University of South Florida Major Professor: Paul Solomon, Ph.D. Andrew Artis, Ph.D. Richard Plank, Ph.D. Michael Coovert Ph.D. Sajeev Varki Ph.D. James Hensel, Ph.D. Date of Approval: December 7 200 9 Keywords: s ales services technology congruity self directed learning Copyright 2010 David E. Fleming

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Dedication This dissertation is dedicated to my family Without their support none of this would have been possible To my wife, Sarah thank you for the sacrifices you have made and your patience throughout this journey To my parents, I thank you for always being supportive and willing to do whatever you could to make this journey easier Finally, this dissertation is dedicated to my children Ryan and Rachael for whose future all of this was done.

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Acknowledgments I am indebt ed to a ll of the people who h ad a hand in me getting through the Ph.D. process. I am thank ful to Dr. Paul Solomon, my dissertation chair, for paving the way for me through the program so that I could attain my desired goals. I thank Dr. Andrew Artis for serving as a mentor and sounding board for the last seven year s ; you r advice and feedback helped me avoid and overcome many obstacles Also, I would like to acknowled ge my original commi ttee members, Dr. Richard Plank and Dr. Michael Coovert for the effort and time they spent helping me complete this dissertation and whose expertise improved the quality of the research I also want to thank Dr. Sajeev Varki and Dr. J ames Hensel for agreeing to serve on my dissertation committee when the need arose I would like to acknowledge Wendy for making sure I always got everything in on time Finally, I would like to thank Dr. Eric Harris for serving as a friend, mentor and c he erleader, and for encouraging me to go beyond my comfort zone and get a Ph.D.

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i Table of Contents List of Tables v List of Figures vi List of Appendices vii Abstract vi ii Chapter 1 1 Introduction 1 Research on Technology in Services and Sales 2 Personification of Firms 11 Importance of Perceived Attitudes 12 Employee Learning 13 Purpose 14 Research Questions 14 Theory 15 Contributions to Marketing 18 Academicians 18 Managers 20 Organization of Dissertation 22 References 24

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ii Cha pter 2 How Managers Influence Employee Perceptions of the Firm 31 Introduction 31 Literature Review Communications Theory 33 Service Marketing 36 Technology Perceptions 38 Model Development 41 Methods 47 Sample 47 Measures 50 Analysis/Findin gs 50 Discussion 57 Implications 57 Academic 57 Managerial 59 References 61 Chapter 3 Employee Perceptions and Its Effect on Their Use of Self Directed Learning 67 Introduction 67 Literature Review 69 Self Directed Learning 69 Affinity for Tec hnology 72 Model Development 74 Method 78

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iii Sample 78 Measures 79 Analysis/Findings 80 Confirmatory Factor Analysis 80 Structural Equation Models 83 Nested Regression Models 87 Discussion 91 Contributions 93 Future Research 95 References 96 Chapter 4 The Effect of Customer Perceptions on Service Outcomes 102 Introduction 102 Literature Review 104 Personification of Firms 104 Individual Perceptions of Technology 105 Self Congruity 109 Service Experience 110 Service Outcomes 112 Mo del Development 113 Methods 118 Sample 118 Measures 120 Analysis 121

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iv Findings 123 Confirmatory Factor Analysis 123 Structural Equations Models 127 Parcel Models 130 Nested Regressions 132 Discussion/Limitations 134 Implications 136 Aca demic 136 Managerial 137 References 139 About the Author End Page

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v List of Tables Table 1 Technology in the Services Literature 4 Table 2. T echnology in the Sales Literature 8 Table 3. Customer Contact Employee Demographics 49 Table 4. Man ager Demographics 49 Table 5. Confirmatory Factor Analysis Results 52 Table 6. Stru ctural Equation Model Result 55 Table 7. Nested Regression Results 56 Table 8. Demographics 79 Table 9. Confirmatory Factor Analysis Results 82 Table 10. Structural Equation Model Results 85 Table 11. Nested Regression Results 90 Table12. Hypothesis Results 93 Table 13. Demographics 119 Table 14. Confirmatory Factor Analysis Results 126 Table 15. Structural Equation Model Results 129 Table 16. Parcel Model Results 132 Table 1 7. Nested Regression Findings 133 Table 18. Hypothesis Outcomes 135

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vi List of Figures Figure 1 Schramm Model of Communication 35 Figure 2 Conceptual Model 46 Figure 3 Hypothesized Model 47 Figure 4. Empirical Model 78 Figure 5. Conceptual Model 1 1 8

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vii List of Appendices Appendix 1 Manager and Customer Contact Employee Survey Items 65 Appendix 2 Survey Items 100 Appendix 3 Customer Survey Items 145

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viii Developing the Nomological Network of Perceived Corporate Affinity f or Technol ogy: A Th ree Essay Dissertation David E. Fleming ABSTRACT Technology is changing the face of both the sales and service domains. Honebein and Cammarano (2006) note that properly implemented self service technologies serve dual purposes of decreasing firm overhead c osts, while simultaneously engaging the customer in a way encourages the co create of value for both parties. To get these benefits stakeholders must be willing to adopt and use the technologies that are available. Traditionally, this has lead to the resea However, according to a recent article by Woodall, Colby and Parasuraman (2007), consumers are now demanding more technology based options and becoming more technologically savvy. This changes the research focus to answ can firms be seen as able to deliver technology based options effectively, efficiently and Th e purpose of this dissertation is to examine the role of stakeholder perceptio ns of firm attitudes toward technology in answering this question. Perceived corporate affinity for technology positive affect toward technology, and was developed and validated in sales and services contexts using samples of both employees and customers.

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ix The studies of this dissertation test potential antecedents, consequence and boundary conditions of stakeholder perceptions of corporate affinity for technology in thre e key groups, namely managers, employees and customers. To accomplish this purpose, the following research questions, one for each key group of stakeholders, were proposed for this study: 1) Do manager perceptions of corporate affinity for technology influen ce employee perceptions of corporate affinity for technology? 2) Do employee perceptions of corporate affinity for technology influence employee learning behavior? 3) Do customer perceptions of corporate affinity for technology influence how they perceive the qu ality of the service delivery and their rating of other key customer service outcomes? Separate conceptual models were developed and te sted to answer these questions.

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1 Chapter One I ntroduction Technology is changing the face of both the sales and se rvice arenas. For (2009), there are over 406,000 ATMs in the United States, and these ATMs were used almost 14.7 billion transactions. This is an incredible usage of an impersonal self service technology considering the importance placed on the service provider in past academic studies (Harris and Fleming 2005). ATMs are just one example of how technologies have and are continuing to radically change how firms and cus tomers interact: from providing faster and more precise service delivery, to providing service providers and salespeople with more up to date information to share with their clients, to allowing customers to complete transactions at convenient times and lo cations without the physical presence of a service provider. Honebein and Cammarano (2006) note that properly implemented technologies serve a dual purpose. The first is to decrease the overhead costs for the firm through a reduction in employees or increa sed service delivery efficiency and accuracy, while the second purpose is to engage the customer in such a In order for firms to reap these benefits, however, stakeholde rs (both firm employees and customers) must use the technologies that are available. This has traditionally lead researchers to examine the technology specific factors (Curran and

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2 Meuter 2005; 2007) and individual level human characteristics (Parasuraman 2000) that influence technology adoption, and more importantly, use by stakeholders. However, a recent article by Woodall, Colby and Parasuraman (2007) notes that customers are becoming more technologically savvy and are demanding more technology based s ervice crease in portability, mobility. A recent call for papers in the Journal of Personal Selling and Sales Management highlights the need for research on the impact of technology on the Company Customer, Salesperson Customer, and Sales Force Company interfaces of the selling process, especially on the B2C side of sales; which also highlights the need for research on the role of the firm when it c omes to technology perceptions. Re search on t echnology in s ervices and s ales The marketing literature on technology focuses mainly on two key facets that influence its use, namely personal factors and aspects of the technology itself. There have been many studies of personal technology. Examples of this include dimensions of attitudes toward technology, the work of Heinssen, Glass and Knight (1987) on scale designed to measure the willingness of a person to adopt new technologies, and Edison On the other side are the studies that look at specific traits of the technology that influence whether it will be adopted or used, such as the work of Curran and Meuter (200 5; 2007). As can be seen in T ables 1 and 2 most of the research on

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3 technology in the services and sales domains also examines these two types of factors as well. The focus on these facets is not surprising given that the traditional research question guidi s examines how technology impacts firm performance either through improving employee effectiveness or positive customer outcomes. However, the recent work by Woodall, Colby and Parasuraman (2007) notes that service customers are technologically savvy and predict that the services domain will experience a significant shift as customers demand more technology based Now the challenge facing firms is finding ways to show customers that the firm is capable of effectively, efficiently and securely delivering on this new generation of services. A recent call for papers in the Journal of Personal Selling and Sales Management highlights the need for research on the impact of technology on the Company Customer, Salesperson Customer, and Sales Force Company interfaces of the selling process, especially on the B2C side of sales. Again this shows the need for research on the role of the firm when it comes to technology.

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4 Table 1 Technology in the Services Literature Author(s) Date Type Sample Key Findings Bign, Alds & Andreu 2008 Quantitative Managers Relat ionship intensity and environmental factors enhance e business adoption. E communication positively influences e procurement in supply chains. Vlachos & Vrechopoulos 2008 Quantitative Customers Content quality, contextual quality, device quality, connect ion quality and privacy concerns positively influence service quality perceptions. Service quality, value and satisfaction have direct effects on behavioral intentions to use the technology Forbes 2008 Quantitative Customers The service failures experie nced by non internet self service technology (sst) customers are different than those experienced by customers in traditional retail and e tail settings, and the recovery strategies employed by companies using non internet ssts are also. Post recovery swit ching by non internet SST customers can be high even with a satisfying experience. Timmor & Rymon 2007 Quantitative & Qualitative Students The participants' perception of outcomes, ease of use and technology orientation; the consistency of the new service delivery process with the old; and the perceived image of the provider influence behavioral intentions regarding a new, technology based learning format. Jayasimha & Nargundkar 2007 Conceptual N/A Self service technologies are more likely to be used by c ustomers that have certain demographic profiles, and it is unlikely that all firms have large enough customer bases of these desirable profiles to make implem enting these technologies worth while. Thus, understanding and overcoming the hindrances to adoptio n would help these firms. Ghodeswar & Vaidyanathan 2007 Conceptual N/A A framework of organization buying behavior of innovative medical technology, and proposes that it is influenced by organizational factors, organizational processes, individual charact eristics, group factors, technological factors and the external environment.

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5 Author(s) Date Type Sample Key Findings Hackman, Gundergan, Wang & Daniel 2006 Quantitative Customers Online service value is influenced by the online service quality and relate d sacrifice. Online service satisfaction is influenced by online service value and online service quality. Behavioral intentions to use online services are directly influenced by online service quality, online service value and online service satisfaction. Walker & Johnson 2006 Quantitative Customers Customer willingness to use the internet and telephone for financial and shopping services is impacted by the individual's belief in their personal capacity/ability to engage with the service system, perceived risks, relative advantages, and extent to which contact with service personnel is preferred or seen as necessary. Harris, Mohr & Bernhardt 2006 Quantitative Customers Online participants blamed themselves more for service failures, and expect less failur e recovery than offline consumers. Matthing, Kristensson, Gustafsson &. Parasuraman 2006 Quantiative & Qualitative Customers Technology readiness can be used as a tool for identifying customers who are innovative in terms of attitudes and behaviors. Custo mers that score highly on technology readiness are able to develop highly creative new service ideas in terms of both quantity and quality. Gerrard, Cunningham & Devlin 2006 Qualitative Customers The key factors identified as reasons that customers do not use internet banking are perceptions about risk, the need for it, lacking knowledge, inertia, inaccessibility, lack of a human touch, pricing and fatigue with information technology. Forbes, Kelley & Hoffman 2005 Quantitative Customers E tail customers experience different service failures than customers in traditional retail settings, e tail firms employ different recovery strategies than traditional retail firms, and post recovery switching by e tail customers can be high even with a satisfying experie nce.

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6 Author(s) Date Type Sample Key Findings Curran & Meuter 2005 Quantitative Customers Many factors need to be considered when introd ucing technologies into service encounters, and these factors may differ depending on the technology and its stage of adoption. Bansal, McDougall, Dikolli & Sedatole 2004 Quantitative Customers Models that examine the antecedents and consequences of satisfaction in offline settings applicable to online settings. Web site characteristics impact behavioral outcomes. Web site customer service only influenced retention/referral outcomes. Web site customer service may be necessary but not sufficient to attaining positive outcomes in online settings. Sweeney & Lapp 2004 Quantitative Customers The types of incidents that lead to perceptions of high service quality are active marketing oriented aspects, while the incidents leading to perceptions of low service quality tend to be technically oriented. Gummerus, Liljander, Pura & van Riel 2004 Quantitative Customers Customer loy alty to contend based service websites is based on satisfaction, but satisfaction is influenced by trust. Need fulfillment, responsiveness, security and technical functionality of the site impact trust. Rexha, Kingshott & Aw 2003 Quantitative Customers Th e adoption of electronic banking is directly impacted by trust and c ustomer satisfaction indirectly influenced the adoption of electronic banking through its impact on trust. Drennan & McColl Kennedy 2003 Quantitative Customers The Internet significantly influences the perceived performance of service firms, but the aspects of internet use that influences this relationship varies by the type of service offered. Retail Services transactional functions are positively related to increases in perceived perf ormance. Professional Health Service the ability to search for information on products/services is positively related to perceived performance. Lee & Allaway 2002 Quantitative Students The adoption process of self service technologies is improved when p otential customers are provided with high predictability, high controllability and high outcome desirability.

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7 Author(s) Date Type Sample Key Findings Thornton & White 2001 Quantitative Customers Customer orientations influence which of financial distribu tion channels customers use. In of their orientations, could potentially reduce the operating costs of offering multiple financial distribution channels by allowing the firm to specialize in the cha nnels that these high value desire. Fisk 1999 Conceptual N/A Customer desires for technology are something to which servi ce marketers must pay attention An over emphasis on technology and ignorance of customer needs can be disastrous. "... technology is merely the means to the end and not the end in itself."

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8 Table 2 Technology in the Sales Literature Author(s) Date Type Sample Key Findings Rapp, Agnihotri & Forbes 2008 Quantitative Salespeople, Managers SFA usage directly impacts effort and reduces t he number of hours worked. CRM use positively impacts adaptive selling, but experience moderates this relationship. Moutot & Bascoul 2008 Quantitative Salespeople SFA implementation in CRM creates a mostly negative effect of SFA reporting but positive eff ects of SFA call planning and product configuration Hunter & Perreault 2006 Quantitative Salespeople A salesperson's technology orientation directly impacts internal role performance, and it affects performance with customers through a mediated path via t he effective use of information and smart selling (i.e. planning and adaptive selling). Ko & Dennis 2004 Quantitative Salespeople SFA positively influences sales performance, but expert ise moderates this relationship. Jones, Sundaram & Chin 2002 Quantita tive Salespeople Perceived usefulness, attitude toward the new system, and compatibility were found to be antecedents of intention to use new SFA systems prior to implementation. Personal innovativeness, attitude toward the new system, and facilitating con ditions are antecedents to the use of new SFA systems. Widmier, Jackson & McCabe 2002 Quantitative Salespeople Most firms are using some form of SFA, usually in the form of contact management, generating sales proposals, creating presentations, sales call s and expense reporting and less frequently in sales route planning and automated sales plans Kennedy & Deeter Schmelz 2001 Qualitative, Quantitative Industrial Buyers Antecedents to buyer use of the internet include self perceived innovativeness, conveni ence seeking, pressure to reduce costs, influence of others in the firm, and supplier support. Shoemaker 2001 Conceptual N/A Technology should serve the role of integrating knowledge management, transaction and customer relationship processes. Erffmeyer & Johnson 2001 Quantitative Executives, Managers Most firms are pleased with their SFA implementations, and those firms who have set goals with their SFA implementation have achieved them.

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9 Author(s) Date Type Sample Key Findings Rivers & Dart 1999 Quanti tative Managers Several key antecedents to the acquisition of SFA technology were identified, but very few of these were related to the benefits of SFA adoption. Swenson & Parrella 1992 Qualitative Managers, Salespeople Core reasons for adopting new techn ology: (1) Customer orientation: to better serve customers, and (2) productivity: new technology produces sales gains that cover its cost. Wedell & Hempeck 1987 Conceptual N/A Six key factors for successful SFA programs: remote access, e mail, word proces sing & spreadsheet software, time management software, cellular telephones, and adequate training of staff. Potential hard & soft dollar savings from the use of SFA.

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10 In order to examine this phenomenon, the author draws on the recent work of Fleming an d Artis (forthcoming). Their work extended the idea of affinity for technology by developing and validating a measure of customer and employee perceptions of the perception individuals have o (p 8). This construct technology, which is very different from how the individual feels about technology. This construct was developed and validated in pretests utilizing both qualitative and quantitative methods based on well respected scale development procedures (Churchill 1979; Jarvis, MacKenzie and Podsakoff 2003; MacKenzie 2003; Rossiter 2003; Segars 1 997). According to their qualitative study used to the measure of this construct, these perceptions of corporate affinity for technology can be derived from many different points of contact with an organization such as advertisements, encounters with emplo yees and contact with managers. Another interesting finding was that customers stated that spanning employees, while the employees thought that mass media was more in formative for customer. This highlights the fact that a gap exists between what customers and employees believe is the role of the boundary spanners in sharing information about technology. The quantitative studies utilized exploratory and confirmatory fa ctor analyses and found that the same eight item solution created the best factor structure for both customers and employees. Correlational analyses found that from a customer perspective this construct is related to both personal affinity for technology ( r=.34) and perceptions of service performance (r=.69). Thus this work proposes that stakeholder

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11 perceptions of corporate attitudes may also play a role not only in stakeholder use and adoption of technology, but key outcome perceptions and behaviors. Whil e their work did not test the relationships between employee perceptions of corporate affinity for technology and other variables, the customer findings indicate that how an individual perceives a firm relating to technology does impact how they perceive t he firm in other areas. This is important in the service setting because what the customer contact employee thinks the firm as values will influence both the methods they use and how they communicate with current and prospective clients. However, no work has yet been done to develop a nomological network for this construct. Personification of f irms The consumer behavior literature frequently contains research based on the notion that consumers instill innate objects with human characteristics, and many o f these articles utilize measures of human traits to evaluate firms or brands. Granted, it is impossible for an inanimate object such as a company to actually possess an attitude towards anything. However, people do tend to assign human traits to firms thr ough a process of anthropomorphism (Brown 1991). Brown also states that giving human characteristics to inanimate objects seems to be a universal occurrence and that the personification of firms allows people to anthropomorphize objects in order to better express their evaluative judgments. McGill (2000) notes that people place brands in to natural categories just as they do other people and animals. According to products, brands, stores and other commercial objects in terms of human attributes is

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12 instance, the literature on brand personality (Aaker 1997) relies on the notion that customers assi g n personality traits to firms. The i mportance of p erceived a ttitudes To date, most studies that apply human qualities to firms only assume that customers imbue them with human traits. For example, studies that draw on the self congruity literature (Sirgy 1980; Sirgy and Samli 1985) are interested in the extent to which the traits perceived in the product or store are congruous with either ideal, social or actual self of the purchaser. Other studies (Ekinci and Riley 2003; Harris and Fleming 2005) have shown that congruity between the formation of perceptions of service quality and the likelihood of positive service outcomes such as satisfaction and word of mouth int entions. As of yet, no studies have examined whether the congruity of attitudes possessed by the customer with those they perceive the company to hold towards either objects or ideas are as important to key outcomes as the traits that have been studied. Th is is important as customers develop attitudes and commitments towards causes (e.g. the environment) and objects (i.e. technology) and these customer attitudes influence company communication efforts and actions. For instance, the increase in consumer con cerns about the environment influenced Wal that the company is concern about the issue as well (Gunther 2006). Additionally, understanding how employees form perceptions of firm attit udes towards technology is vital. According to the qualitative pretest by the author most customers learn about the is important to examine whether the activities on the part of firms to generate these

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13 perceived attitudes among customers and employees is a productive investment or a waste of valuable company resources. Employee l earning The ability of salespeople to learn it is at the heart of many key concepts in the sales literature because of the growing need for salespeople to be able to adapt to rapidly changing competitive environments, customer needs and regulatory and firm requirements ( Jones, Brown, Zoltners and Weitz 2005; Marshall, Moncrief and Lassk 1999). Most sales force research on learning has focused on formal training (Lupton, Weiss and Peterson 1999; Cron, Marshall, Singh, Spiro and Sujan 2005) or learning through experience (Turley and Geiger 2006; Sujan, Weitz and Kumar 1994). While th ese are important ways for salespeople and service personnel to learn, they many changing marketplace. Recently, the concept of self directed learning been incorporated into the sales area (Hurley 2002; Artis and Harris 2007) from the adult education field. Self directed learning provides a new insight into salesperson learning by looking at how employees can be responsible for their own learning, implementing that learning to reach their personal and corporate goals and evaluating the outcomes of their learning (Knowles moderators, mediators and outcomes of the use of SDLPs by salespeople. Through the ir detailed review of the self directed learning literature they propose four antecedents, two moderators and one mediator of the use of SDLPs by salespeople. The four individual characteristics they identified as antecedents are learner self directedness, confidence in self directed learning skills, contextual understanding and motivation to learn. The two moderators they propose are environmental turbulence and organizational learning

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14 climate. The moderating variable proposed is willingness to use SDLPs. This model can also be applied to service personnel as both salespeople and service providers are customer contact boundary spanners who fulfill similar roles (Singh and Rhodes 1991). However, this area of research has not examined the role of technology in employee use of self directed learning. Purpose The purpose of this paper is twofold. The first is to de termine if the efforts that corporate executives engage in to create perceptions of firm attitudes actually influence employee and customer behavi ors and outcomes. This finding will determine if these by Woodall, Colby and Parasuraman (2007) and as they attempt to understand how technology impacts the various sales process interfaces. The second purpose of this paper is to develop the nomological network for the construct of perceived corporate affinit y for technology. This work is important as it tests the validity of this new construct beyond the face, content and convergent validities found in the pretest. Also the development and testing of a nomological network shows the importance of the construc t in terms of the strength of its influence on key outcome measures. Research Questions

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15 1) Do manager perceptions of corporate affinity for technology influence employee perceptions of corporate affinity for technology? 2) Do employee perceptions of corporate a ffinity for technology influence employee learning behavior? 3) Do customer perceptions of corporate affinity for technology influence how they perceive the quality of the service delivery and their rating of other key customer service outcomes? Theory Give n the differing nature of the three key stakeholder groups that serve as the foci of these studies, and the disparate types of variables included in each investigation; it was necessary to draw on a wide array of theories in developing the various parts of the nomological network. To this end, each study and the theories applied in them will be discussed separately. corporate affinity for technology, the core theoretical basis i s the Schramm model of communication (Schramm 1954) The Schramm model draws heavily on the Shannon key parts. Six of these components are necessary for communication and on e creates the entropy in communications that Shannon sought to understand from a probabilistic sense. The six parts necessary for communication are 1) an information source, 2) a message, 3) a transmitter, 4) a signal, 5) a receiver and 6) a destination. T he seventh key component of a communication system that they identify is noise and is not necessary; in fact it is a

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16 detriment to the effective transmission of the signal between the transmitter and receiver. Schramm (1954) added the components of a feedba ck loop from the destination to the information source and a shared field of experience between the source and destination. This model serves as a basic framework for how manager perceptions of corporate affinity for technology shape employee perceptions o f corporate affinity for technology. In addition to this model, the moderating effects of examined noise factors are explained via two additional theoretical bases. The first is implicit attitudes (Fazio, Jackson, Dunton and Williams 1995); these studies the target stimulus is incongruous with their own belief about the prime they have received, then it interferes with their ability to communicate the category of the target, and similar results have been found in a marketing context. This is the basis for the moderating effect of manager personal affinity for technology on the central relationship between manager perceptions of corporate affinity for technology and employee perceptions of corporate affinity for technology. The second theoretical basis for the noise components is the concep t of selective attention. Accor ding to Triesman (1969), individuals are only able to process a small portion of the information they receive at any given time and therefore must choose what information to which they are going to attend. This theory is used to explain how employee personal affinity for technology moderates the relationship between manager perceptions of corporate affinity for technology and employee perceptions of corporate affinity for technology. The core theoretical basis of the study examining how employee perceptions of corporate affinity for technology influence their learning behaviors comes from the work of Artis and Harris (2007). Artis and Harris (2007) e xtended the notion of self directed

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17 learning projects (SDLPs) into the sales area by providing a conceptual model of the antecedents, moderators, mediators and outcomes of the use of SDLPs by salespeople. Through their detailed review of the self directed learning literature they propose four antecedents, two moderators and one mediator of the use of SDLPs by salespeople. The four individual characteristics they identified as antecedents are learner self directedness, confidence in self directed learning sk ills, contextual understanding and motivation to learn. The two moderators they propose are environmental turbulence and organizational learning climate. The moderating variable proposed is willingness to use SDLPs. This model serves as a basic framework t hat guides the conceptualization of how employee perceptions of corporate affinity for technology influence the use of SDLPs by employees. A secondary theoretical underpinning for this study is social exchange theory (Thibaut and Kelley 1959), which state s that relationships involve a mutual give and take between the two parties involved. In this case, if the employee believes that the firm provides something for the employee, then the employee will reciprocate to the firm by taking advantage of this oppor tunity. In this case, if the firm shows an affinity for technology, then the employee will utilize the available technology for the betterment of the firm (i.e. learning). The core theory underlying the third study of how customer perceptions of firm affi nity for technology influences service performance perceptions is through its role as a signal to customers. Signaling theory is based in the economic study of asymmetric information conditions between buyers and sellers (Spence 1974). It is based on the notion that sellers know their true product quality prior to the sale, but buyers do not; especially if these products contain experience properties (such as services), which can

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18 only be evaluated during consumption (Nelson 1970). One way firms can overc ome this information gap is to send signals about their service quality. A variety of signals have been tested such as price (Milgrom and Roberts 1986), advertising (Ippolito 1990), and warranties (Boulding and Kirmani 1993). A second major theory in t his study is the notion of self congruity (Sirgy 1982). The key self In his work, the ideal self is defined as how an individual would like to see himself or herself; t he actual self is defined as how an individual views himself or herself; and the social self is defined as how an individual would like others to see him or her. His work revealed that consumers were more likely to select products that possess traits that are consistent with positive aspects their self image. He also showed that this congruity has a strong influence on purchase motivation. This theory is used to explain how customer personal affinity for technology serves as a moderator of the relationship between customer perceptions of firm affinity for technology and service performance perceptions. Contributions to Marketing As with the theory section, the contributions section will be broken down by study due to the different stakeholder groups involv ed in each. Also the implications of this study for academicians and managers will be examined separately. Academicians The first study has several important implications for academicians. The first is that this paper explores the role of technology in the employee attitude toward technology to employees who then pass this information to customers.

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19 The other major academic implication of this paper comes from the appl ication of a well known theory to a new area of study. This paper applies the communication model of Schramm (1954) employees to form customer contact employee perceptions of firm atti tudes. This is important as it draws in a model from another area into the study of services marketing and serves as a theoretical reference point for future research into the how internal marketing communication occurs and the potential threats to the cl ear transmission of messages between the firm and customer. The second study contributes to the literature by developing and testing a model that extends the current thinking on what drives boundary spanning employee use of self directed learning projects. The model shows that getting employees to engage in self directed learning projects is both a selection issue and an internal marketing issue. On the selection side, this model shows the importance of hiring and retaining those customer contact employees who have a high affinity for technology as they are more likely to engage in the use of self directed learning projects that benefit the firm in addition to being more open to the increasingly important technological advances. On the internal marketing sid toward technology to help increase employee use of SDLPs. The findings of the third study have several important implications for academicians. The first is that this paper introduces a new class of potential antecedents to the formation of customer perceptions of service outcomes. Specifically, this paper shows that perceived firm attitudes my influence customer perceptions of service quality, and through service quality perceptions in directly influence other key outcomes of

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20 interest to the firm. The second key finding is that the congruity between individual attitude and perceived firm attitude determines strength of this antecedent relationship. While this may not seem like a big cont ribution given the extensive literature on congruity theory, it is actually very important as it shows that the influence of congruity extends beyond the match of customer and firm trait s (usually personality traits). Finally, each study contributes to the process of developing the nomological network for the new construct of perceived corporate affinity for technology. Given that this is a new construct with very little empirical research, it is important that it be thoroughly tested in order to assess its convergent, discriminant and construct validity. It is also important to test this new construct to determine what, if any, affects it will have on the current knowledge base of the field. Managers The first study in this dissertation contains several b enefits for managers. The first benefit is that this study shows the importance of managers in the process of sharing information with employees. However, this information is not just what the firm expects from employees in terms of performance and activit ies as shown in past studies, but also information about the attitudes that the firm has towards objects or causes. This is vital as firm attitudes towards causes, such as the environment, are believed to be vital to increasing patronage. Additionally, fir m attitudes towards technology, as communicated by frontline employees, should influence customer usage of technological offerings by the firm. As noted by Honebein and Cammarano (2006) properly implemented technologies can be a cost savings for firms as w ell as a means to cocreate value by involving customers, which should result in more satisfied and loyal patrons. Another of the key benefits of this study for managers is that it shows that the

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21 role of managers in sharing information with employees about the firm goes beyond just telling employees what the company expects. Specifically it identifies that how the manager personally feels about the message they are sending affects the signal that the employee receives and in turn the message that is passed o n to the customer. Thus, managers must be cognizant of their own feelings in regards to technology (or other objects/causes) when sharing firm attitudes about technology (or other objects/causes) with their customer contact employees and be mindful of the impact of their personal attitudes on the message they are delivering. A final key benefit of this study for managers is that it highlights the importance of employee personal attitudes on the reception of communications about firm attitudes are received. This is relevant, as according to the internal marketing literature, these perceptions of the firm are then transmitted to the customer and can influence service delivery perceptions (e.g. Lai 2006). Thus, managers need to be aware of how their employees f eel about technology as employee, and may need to spend more time communicating the message to those employees whose personal attitudes are not inline with the message that the firm is trying to convey to customers. First, the model shows how both individual and perceptions of firm level affinity for technology can improve employee profitability by imp roving their use of SDLPs that result in better knowledge, which translates into more sales. This study also shows how important personal affinity for technology and perceptions of corporate affinity for technology are in creating a competitive advantage. The extended knowledge base that

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22 employees develop through the use of voluntary and scanning SDLPs is a competitive advantage that is difficult for competitors to overcome or replicate because it is based on ed to be successful. For managers, the third study contains some important insights as well. First, the study shows the importance of customer perceptions of firm attitudes, in this case toward technology, but it could reasonably be extended to customer pe rceptions of firm attitudes toward objects, ideas or causes. This paper provides empirical evidence of the importance of customer perceptions of firm attitudes and links these perceptions to their impact on key outcomes that relate directly to customer att raction, retention and profitability. This provides managers with evidence to present to their shareholders in defense of their efforts to project certain attitudes to customers. A second benefit that this paper provides managers is that it shows the impor attitudes as well because personal attitudes serve to enhance or limit the strength of attitude toward technology, if th e core markets of the firm do not have favorable personal affinity for technology are frivolou s at best and harmful at worst. Organization of the Dissertation The re st of the dissertation is organized as follows. Each research question mentioned previously is examined via a complete, journal ready style article. Each article contains and integrates the literature pertinent to the specific research question as well as the models that will be used to test these relationships. Each article also includes the

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23 methodology and measures to be used to test the models and a discussion of the implications of the study to both academicians and managers.

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24 References Aaker, J L. (1997). Dimensions of brand p ersonality Journal of Marketing 34 (3), 347 356. Artis, A. B. & Harris E. G. (2007) Self d irected l earning and s ales f orce p erformance: An i ntegrated f ramework Journal of Personal Selling and Sales Management 29 (1), 9 24. Bansal, H. S., McDougall, G. H. G., Dikolli S. S., & Sedatole K. L. (2004). Relating E satisfaction to b ehavioral o utcomes: An e mpirical s tudy The Journal of Services Marketing 18 (4/5), 290 302. Bign, J. E. Alds J. & Andreu L. (2008). B2B s ervices: IT a doption in t ravel a gency s upply c hains The Journal of Services Marketing 22 (6), 454 464. Boulding, W & Kirmani A. (1993). A c onsumer s ide e xperimental e xamination of s ignaling t heory: Do c o n sumers p erceive w arranties as s ignals of q uali ty? The Journal of Consumer Research 20(1), 111 123. Brown, D E. (1991). Human universals New York, NY: McGraw Hill. Journal of Marketing Research 16 ( 1), 64 73. Cron, W L., Marshall, G. W., Singh, J., Spiro R. L., & Sujan H. (2005). Salesperson s election, t raining, and d evelopment: Trends, i mplication, and r esearch o pportunities Journal of Personal Selling & Sales Management 25(2), 123 136. Curra n, J M. & Meuter M. L. (2005). Self service t echnology a doption: Comparing t hree t echnologies Journal of Services Marketing 19 (2), 103 113. Curran, J M. & Meuter M. L. (2007). Encouraging e xisting c ustomers to s witch to s elf s ervice t echnologies: Pu t a l ittle f un in their l ives The Journal of Marketing Theory and Practice 15(4), 283 298. d'Astous, A. & Levesque M. (2003) A s cale for m easuring s tore p ersonality Psychology and Marketing 20(5), 455 469.

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25 Drennan, J. & McColl Kennedy J. R. (2003) The r elationship b etween i nternet u se and p erceived p erformance in r etail and p rofessional s ervice f irms The Journal of Services Marketing 17 (2/3), 295 311. Edison, S. W. & Geissler G. L. (2003). Measuring a ttitudes t owards g eneral t echnology: a ntece dents, h ypotheses and s cale d evelopment Journal of Targeting, Measurement and Analysis for Marketing 12 (2), 137 156. Ekinci, Y. & Riley M. (2003). An investigation of self concept: A ctual and ideal self congruence compared in the context of service eva luation Journal of Retailing and Consumer Services 10 (4), 201 2 14. Erffmeyer, R C. & Johnson D.A. (2001) An e xploratory s tudy of s ales f orce a utomation p ractices: Expectations and r ealities Journal of Personal Selling & Sales Management 21(2), 167 175. Fazio, R H., Jackson, J. R., Dunton B. C., & Williams C. J. (1995) Variability in automatic activation as an unobtrusive measure of racial attitudes: A bona fide pipeline ? J ournal of Personality and Social Psychology 69 (6), 1 013 1 027. Fisk, R. P. (1999). Wiring and g rowing the t echnology of i nternational s ervices m arketing The Journal of Services Marketing 13 (4/5), 311 318. Fleming, D & Artis A B. ( forthcoming ). Measuring c orporate a ffinity for t echnology: A s cale for c ustomers and e mploy ees Journal of Personal Selling and Sales Management e lling: Threats and Forbes, L. P. (2008) When s omething g oes w rong and n o o ne is a round: Non internet s elf service t echnology f ailure and r ecovery The Journal of Services Marketing 22(4), 316 327. Forbes, L P., Kelley S. W., & Hoffman K. D. (2005). Typologies of e commerce r etail f ailures and r ecovery s trategies The Journal of Services Marketing 19 (5), 280 292. Gerrard, P. Cunningham J. B & Devlin J. F. (2006). Why c onsumers are n ot u sing i nternet b anking: A q ualitative s tudy The Journal of Services Marketing 2 0 (3), 160 168. Ghodeswar, B. M. & Vaidyanathan J. (2007). Organisational a doption of m edical t echnology in the h ealthcare s ec tor Journal of Services Research 7 (2), 57 81. Goldman, R. D., Platt B. B., & Kaplan R. B. (1973). Dimensions of a ttitudes t oward t echnology Journal of Applied Psychology 57 (2), 184 187.

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26 Gummerus, J. Liljander, V., Pura M., van Riel A. (2004). Cu stomer l oyalty to c ontent based w eb s ites: The c ase of an o nline h ealth care s ervice The Journal of Services Marketing 18 (2/3), 175 186. Gunther, M. (2006) Mart s ees g CNNMoney.com. available at http://money.cnn.com/2006/07/25/news/companies /wal mart short.fortune/ Hackman D Gundergan S. P., Wang P., & Daniel K. (2006). A s ervice p erspective on m odeling i ntentions of o n line p urchasing The Journal of Services Marketing 20( 7), 459 470. Harris, E G. & Fleming D. E. (2005) Assessin g the h uman e lement in s ervice p ersonality f ormation: Personality c ongruency and the f ive f actor m odel Journal of Services Marketing 19 (4), 187 198. Harris, K. E., Mohr L. A. & Bernhardt K. L. (2006). Online s ervice f ailure, c onsumer a ttributions and e xpectations The Journal of Services Marketing 20 (7), 453 458. Heinssen, R. K., Glass C. R., & Knight L. A. (1987) Assessing c omputer a nxiety: Development and v alidation of the c omputer a nxiety r ating s cale Computers in Human Behavior 3 (1), 49 59. Honebein P C & Cammarano R. F. (2006) Customers at w ork Marketing Management 15(1), 26 31. Hunter, G. K. & Perreault, Jr. W. D. (2006) Sales t echnology o rientation, i nformation e ffectiveness, and s ales p erformance Journal of Personal Selling & Sales Management 26 (2), 95 113. Hurley, R. F. (2002). Putting p eople b ack i nto o rganizational l earning Journal of Business and Industrial Marketing 17(4), 270 281. Insuran ce Information Institute (2009). Financial Services Fact Book available at http: //www.iii.org/financial2/technology/atm/ Ippolito, P. M. (1990) Bonding and n onbonding s ignals of p r o duct q uality The Journal of Business 63 (1), 41 60. Jarvis, C. B MacKenzie S. B., & Podsakoff P. M. (2003) A c ritical r eview of c onstruct i ndicat ors and m easurement m odel m isspecification in m arketing and c onsumer r esearch Journal of Consumer Research 30 199 218. Jayasimha, K. R. & Nargundkar R. (2007). Adoption of s elf service b ill p ayment t echnologies (SSBPTS): A c onceptual m odel Journal of Services Research 7 (1), 119 134.

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27 Jones, E. Brown, S. P., Zoltners A. A., & Weitz B. A. (2005) The c hanging e nvironment of s elling and s ales m anagement Journal of Personal Selling & Sales Management 25 (2), 105 111. Jones, E. Sundaram S., & Chin W. (2002). Factors l eading to s ales f orce a utomation u se: A l ongitudinal a nalysis Journal of Personal Selling & Sales Management 22(3), 145 156. Kennedy, K N & Deeter Schmelz, D. R. (2001) Descriptive and p redictive a nalyses of i ndustrial b uyers' u s e of o nline i nformation for p urchasing Journal of Personal Selling & Sales Management 21(4), 279 290. Knowles, M S. (1975) Self d irected l earning: A g uide for l earners and t eacher New York NY : Association Press. Ko, D & Dennis A. R. (2004) Sales f orce a utomation and s ales p erformance: Do e xperience and e xpertise ma tter? Journal of Personal Selling & Sales Management 24 (4), 311 322. Lai, J (2006) Assessment of e p erceptions of s ervice q uality and s atisfaction with e b usiness International Journal of Human Computer Studies 64 (9), 926 938. Lee, J. & Allaway A. (2002) Effects of p ersonal c ontr ol on a doption of s elf service t echnology i nnovations The Journal of Services Marketing 16 (6), 553 572. Lupton, R. A., Weiss, J. E., & Peterson R. T. (1999) Sales t raining e valuation m odel (STEM) : A c onceptual f ramework Industrial Marketing Managemen t 28 (1), 73 86. MacKenzie, S. B. (2003) The d angers of p oor c onstruct c onceptualization Journal of the Academy of Marketing Science 31 (3), 323 326. Marshall, G. W., Moncrief W. C., & Lassk, F. G. (1999) The c urrent s tate of s ales f orce a ctivities Industrial Marketing Management 28 (1), 87 98. Matthing, J. Kristensson, P., Gustafsson A., & Parasuraman A. (2006). Developing s uccessful t echnology based s ervices: The i ssue of identifying and i nvolving i nnovative u sers The Journal of Services Marke ting 20 (5), 288 297. McGill, A. L. (2000) Counterfactual r easoning in c ausal j udgments: Implications for m arketing Psychology & Marketing 17 (4), 323 343. Milgrom, P. & Roberts J. (1986) Price and a dvertising s ignals of p roduct q uality The Journal of Political Economy (4), 796 821.

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28 Moutot, J. & Bascoul G. (2008) Effects of s ales f orce a utomation u se on s ales f orce a ctivities and c ustomer r elationship m anagement p rocesses Journal of Personal Selling & Sales Management 28(2), 167 184. Nelson P. (1970) Information and c onsumer b ehavior J ournal of Political Economy 78(2), 311 329. Parasuraman, A. (2000). Technology r eadiness i ndex (TRI): A m ultiple i tem s cale to m easure r eadiness to e mbrace n ew t echnologies Journal of Service Research 2 ( 4), 307 320. Rapp A Agnihotri R., and Forbes L. P. (2008). The s ales f orce t echnology p erformance c hain: The r ole of a daptive s elling and e ffort The Journal of Personal Selling & Sales Management 28 (4), 335 350. Rexha, N. Kingshott R. P. J. & Aw A. S. S. (2003). The i mpact of the r elational p lan on a doption of e lectronic b anking The Journal of Services Marketing 17 (1), 53 65. J ournal of Personal Selling & Sales Management 19 (2), 59 73. Rossiter, J. R. (2003) The C OAR SE p rocedure for s cale d evelopment in m arketing International Journal of Research in Marketing 19 (4), 305 335. Schramm, W. L. (1954) How communication work s I n W. Schramm (Ed.), The process and effects of mass communication Urbana, IL: University of Illinois Press. Segars, A. H. (1997). Assessing the u nidimensionality of m easurement: A p aradigm and i llustration within the c ontext of i nformation s ystems r e search Omega 25 (1), 107 121. Shannon, C. E. (1948) A m athematical t heory of c ommunication Bell System Technical Journal 27 379 423 & 623 656. Shoemaker, M. E. (2001) A f ramework for e xamining IT enabled m arket r elationships Journal of Personal Se lling & Sales Management 21 (2), 177 185. Singh, J. & Rhoads G. K. (1991) Boundary r ole a mbiguity in m arketing o riented p ositions: A m ultidimensional, m ultifaceted o perationalization Journal of Marketing Research 28 (3), 328 338. Sirgy, M. J. (1980) Self c oncept in r elation to p roduct p reference and p urchase i ntention I n V. V. Bellur (Ed.), Developments in Marketing Science Vol. 3 (pp. 350 354). Marquette, M I: Academy of Marketing Science

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29 Sirgy, M. J. (1982) Self concept in c onsumer b ehavior: A c ritical R eview The Journal of Consumer Research 9 (3), 287 300. Sirgy, M. J. & Samli A. C. (1985) A p ath analytic m odel of s tore l oyalty i nvolving s elf concept, s tore i mage, g eographic l oyalty, and s ocio economic s tatus Journal of the Academy of Marke ting Science 13 (3), 265 291. Spence, A. M. (1974). Market signaling: I nformational transfer in hiring and related screening processes Cambridge, MA: Harvard University Press. Sujan, H. Weitz B. A., & Kumar N. (1994). Learning o rientation, w orking s m art, and e ffective s elling Journal of Marketing 58(3), 39 52. Sweeney, J. C. & Lapp W. (2004) Critical s ervice q uality e ncounters to the w eb: An e xploratory s tudy The Journal of Services Marketing 18 (4/5), 276 289. Swenson, M. J. & Parrella A. (19 92) Cellular t elephones and the n ational s ales f orce Journal of Personal Selling & Sales Management 12 (4), 67 74. Thibaut, J. J. & Kelley H. H. (1959). The s ocial p sychology of g roups New York, NY: John Wiley and Sons. Thornton, J & White L. (2001 ) Customer o rientations and u sage of f inancial d istribution c hannels The Journal of Services Marketing 15 (3), 168 185. Timmor, Y. and Rymon T. (2007) To d o or n ot to d o: The d ilemma of t echnology based s ervice i mprovement The Journal of Services Mar keting 21 (2), 99 111. Treisman, A. M. (1969) Strategies and models of selective attention Psychological Review 76 (3), 282 299. Turley, D. & Geiger S. (2006) Exploring s alesperson l earning in the c lient r elationship n exus European Journal of Market ing 40(5/6), 662 681. Vlachos, P. A. & Vrechopoulos A. P. (2008) Determinants of b ehavioral i ntentions in the m obile i nternet s ervices m arket The Journal of Services Marketing 22(4), 280 291. Walker R. H. & Johnson L. W. (2006) Why c onsumers u se and d o n ot u se t echnology enabled s ervices The Journal of Services Marketing 20 (2), 125 135. Wedell, A. & Hempeck D. (1987). Sales f orce a utomation -Here and n ow Journal of Personal Selling & Sales Management 7(2), 11 16.

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30 Widmier, S. M., Jackson, D. W., & McCabe D. B. (2002). Infusing t echnology into p ersonal s elling Journal of Personal Selling & Sales Management 22 (3), 189 198. Woodall, R. D., Colby C. L., & Parasuraman A. (2007). E volution" to r evolution: Capitalize on the i mminent e ra of e xplosive e s ervices g rowth Marketing Management 16 (2), 29 34.

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31 Chapter Two: Developing the Nomological Network of Perceived Cor porate Affinity For Technology: Study 1 How M anagers I nfluence E mployee P erceptions of the F irm As technology become s an ever more important part of the service sector and service delivery, it is increasingly imperative that firms understand how their customer prior research has shown t hat employees share their perceptions of the firm with customers. Other studies have found that, according to customers, employees are often a draws on communications the ory and internal marketing literature to develop and test a model of the importance of managers in the formation of employee perceptions of firm affinity for technology. Introduction Technology has become a revolutionized the service experience for consu mers. Woodall, Colby combines the mobility, portability, personalization and collaboration aspects of new services will be the shape of the future in the service industry. As noted by Honebein and Cammarano (2006), properly implemented self service technologies serve a dual

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32 purpose. The first is to decrease the overhead costs for the firm through a reduction in emplo yees, while the second purpose is to engage the customer in such a way that they and Parasuraman (2007) note, customers are becoming more technologically savvy, t he challenge facing firms is no longer getting customers to use the technology available; but rather it is to be seen as A qualitative pretest of bank customers reported that most of the information customers got about the ba relationship with and use of technology came from customer contact employees, while most employees thought customers learned about the technology use of the bank through advertisements and direct mailings. This finding suggests that it is important fo r employees to understand their roll in sharing information with customers and that they have a proper perception of the firm and its relationship with technology so that this information can be passed on to customers. However, no study has examined the ro le technology that is communicated to the customer. The purpose of this paper is to examine the importance of managers in the s affinity for technology. By drawing on communications theory and internal marketing literature, this paper will examine how a inity for technology and how both manager and employee personal affinity for technology influence this relationship. Literature Review

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33 Communications t heory In the study of communications various transmission models have been developed to explain how me ssages are sent and received. One of the by Warren Weaver (Shannon and Weaver, 1963) as Weaver model in the social sciences. This model was originally developed to explain problems in telecommunications using probability theory. Schramm (1954) adapted their m odel to explain mass communication and it has since been adopted by marketing. For instance, several integrated marketing communication texts use a version of the Schramm model to explain the communications process involved in advertising and promotion (Be lch and Belch 2006; Solomon, Cornell and Nizan 2009). However, a similar application has not been used in the internal marketing communication literature. The Shannon Weaver (1963) model contains seven key parts, six of which are necessary for communic ation and one, which creates the entropy in communications that Shannon (1948) sought to understand from a probabilistic sense. The six parts necessary for communication are 1) an information source, 2) a message, 3) a transmitter, 4) a signal, 5) a receiv er and 6) a destination. According to Shannon (1948) an information source produces a message that is communicated to the receiving terminal to be passed along ultimately to the destination. The message is the information that the source wishes to share w ith the destination. The transmitter is responsible for encoding the message in such a way that it can be sent over the channel that carries the signal. The signal is the encoded form of the message that can be sent over the channel or medium of

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34 communicat ion to the receiver. The receiver is responsible for decoding the signal and turning it into a message that can be understood by the destination. The destination is the person the message is targeted toward. The seventh key component of a communication sys tem that they identify is noise and is not necessary; in fact it is a detriment to the effective transmission of the signal between the transmitter and receiver. Shannon (1948) defines noise as any interference that causes a difference between the signal s ent by the transmitter and the signal obtained by the receiver. Schramm (1954) extended the Shannon Weaver (1963) model to make it less mathematical and more applicable to mass communication. The first part that he added was a feedback loop from the destin ation to the information source. He notes that this is a necessary component to allow the source to know that their message is being received by the destination and to adjust the message if it is not being properly received. The second component he added w as the idea of a shared field of experience (such as meanings, beliefs, values or experiences) between the source and destination. He notes that if the parties involved in communication do not share some common understandings, then it is not possible for c ommunication to occur. The relationships in the Schramm (1954) model are shown in Figure 1

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35 Figure 1. Schramm Model of Communication Source Field of Experience Destination Field of Experience Message Signal Message Transmitter Receiver Noise Information Source Destination Feedback

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36 Service m arketing Service marketing varies from the marketing of traditional goods bec ause of the lack of a central tangible product and the importance of the customer contact employee (Harris and Fleming 2005). One of the key aspects of successful services is the treatment of employees as internal customers. Internal marketing is defined by Berry (1981) as the treatment of employees as internal customers of the firm who are consuming other internal products. This view stresses the need to meet these internal customers needs so that they can achieve organizational goals related to external customers. This notion of the employee as a customer of the firm has resulted in a substantial stream of literature. Studies have examined the overlap of this notion with human resources management (George 1990; Zerbe, Dobni and Harel 1998), its role i n turning employees into patrons (Lusch, Boyt and Schuler 1996) and examinations of the factors that help or hinder the internal marketing (Johnson 2008). One key area of study for this stream of literature is on customer contact service employees. The y are an extremely important internal customer group because, as noted by Wasmer and Bruner (1991), they hold a critical role in the service experience and must be sold on the service tual model, Wasmer and Bruner (1991) expound on the triangle model of services marketing developed by Gronroos (1984) and identify six key information flows that occur in services marketing between the customer, employee and firm. The key to this model is the central nature of the customer contact employee in the service experience. The first serves as the foundation of consumer expectations for the service encounter. The second

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37 flow is from the firm to the customer contact employee regarding the expectations the firm has of the employee when they help the customer through the service experience, and this is where management can impart information to the employee that t hey want shared with the customer during service delivery. The third flow is from the customer to expectations of the service encounter are expressed and serve as a guide for th e employee through the service delivery process. The fourth flow is from the employee to the customer that Wasmer and Bruner (1991) describe as service performance where the actual service delivery occurs, and this is where they believe the gap between wha t customers expect and what the employee delivers is most obvious. The fifth flow is from the customer back to the firm in the form of positive and negative feedback. Positive feedback can be in the form of repeat patronization or complementary remarks whi le negative feedback includes things like complaints and switching service providers. The sixth flow is the feedback from the employee to the firm, which they say can be through both formal and informal channels. In their conceptual work they focus on the interactions between flows two and six to explain how organizational culture can be used as a part of internal marketing communication to improve service delivery. Another aspect of the service marketing literature has focused on the importance of managing customer contact employees. According to Hartline and Ferrel (1996) this stream of literature has been based on three major perspectives: the manager employee interface, the employee role interface and the employee customer interface. For the purpose of this paper their findings on the manager employee interface are most important. Particularly, they found that manager commitment to service quality (MCSQ)

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38 was an important determinant of good service delivery and thus customer perceptions of service quali ty. However, they also note that this relationship between manager attitude and employee behavior is mediated by manager actions and employee responses. In a similar vein, Hartline, Maxham and McKee (2000) developed a model of the dissemination of custome r oriented strategy to customer contact service employees, but their model focused on the key paths of influence that result in the employees sharing the values espoused by the firm and management. They found that the primary way to influence customer cont act employees was through the combination of strategy, structure and socialization that uses both formal and informal controls. Taken together, these their subsequent ser vice delivery. Technology p erceptions Edison and Geissler (2003) developed the construct of positive affect toward technology (in general) (p. 140). Their study is concerned with the attitude people hold towar d technology, and they found several antecedents of affinity for technology including optimism, need for cognition, self efficacy, age, and gender. In the only other published study to utilize this scale, Geissler and Edison (2005) found that affinity for technology was positively related to market mavenism. Additionally, this study repeated both the exploratory factor analytic and confirmatory factor analytic techniques used in their first study with similar results, which indicates that the factor structu re of this construct holds up over different found in the original scale development study. However, they are not the first to explore the relationship between people and technology. Other researchers have developed scales

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39 and conducted studies of how individuals interact with technology. The work of Curran and Meuter (2005) identified several characteristics of technologies that influence customer willingness to use th em, such as the ease of use, usefulness and risk associated with certain technologies. Studies by Goldman, Platt and Kaplan (1973); Heinssen, Glass and Knight (1987) and Parasuraman (2000) looked at some of the personal factors that influence an individu curiosity, computer anxiety, optimism, innovativeness, discomfort and insecurity. within the service context f or two reasons. First, their study is concerned with the affect people have for technology; that is they examine the feelings that underlie the human technology interface while other studies (Curran and Meuter 2005) focus on the physical and interface asp ects of the technology. Still other studies focus not on the feelings associated with the human technology interface, but rather the willingness of the individual to adopt the technology (Parasuraman 200 0 ). This is not always an applicable measure of the relationship in the service sector as individuals may be forced to utilize technologies by changes in service offerings or business formats rather than voluntary adopting a technology. For instance, in the mid branches of Bank One were forced to choose between adopting automatic teller machines (ATMs) or paying a $3 per transaction fee to conduct business via a teller. Second, the Edison and Geissler (2003) work defines technology much more generally than other studies. These other studies have taken a much more narrow view of technology and tend to focus on technology as computers, the Internet, or other specific technological tools (e.g., Heinssen, Glass and Knight 1987). These unique characteristics of the affinity f or

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40 technology scale are important as how managers and customer contact employees feel about technology (their affect) should play a much more important role in how they influence employee and perceptions of the firm than if the managers are ready to adopt a technology (i.e., purchase it). Also, the technologies used in service settings may be service kiosks or telebanking. Thus, the construct developed by Edison and Geissler (2003) serves as a good measure for exploring personal attitudes towards technology in the services sector. A proposed extension of the affinity for technology construct is that of customer and employee perceptions of corporate affinity for technology (Fleming and Artis for thcoming) the perception individuals have of the affect (p. 8) This construct places emphasis on which is very different from how the individual feels about technology which is what is was developed and validated utilizing both qualitative and quantitative method s based on well respected scale development procedures (Churchill 1979; Jarvis, MacKenzie and Podsakoff 2003; MacKenzie 2003; Rossiter 2003; Segars 1997). According to the ir qualitative study these perceptions of corporate affinity for technology ca n be derived from many different points of contact with an organization such as advertisements, encounters with employees and contact with managers. The ir quantitative tests exploratory and confirmatory factor analys e s found that the same eight item solut ion created the best factor structure for both customers and employees, and a correlational

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41 analysis found that from a customer perspective this construct is related to both personal affinity for technology (r=.34) and perceptions of service performance (r =.69) Model Development In order to examine the role managers play in the formation of employee perceptions of firm attitudes, it is necessary to draw from the previously mentioned service marketing literature. Specifically, this paper draws on the work of Wasmer and Bruner (1991), Hartline and Ferrell (1996) and Hartline, Maxham and McKee (2000). In the context of this paper, the second flow (from the firm to the employee) that Wasmer and Bruner (1991) identify is the most important as this current stu dy focuses on the role that managers play in the formation of employee attitudes about the firm that are then passed on to the customer. They note that this interface provides the employee with the service performance expectations of the firm and the flow normally occurs through training and the communication of policies, but they do not mention the role of managers in this information flow from the firm whether through formal or informal channels. This work also does not explain the process that underlies the flow from the firm (or its managers) to employees nor was it designed to explain how employees form perceptions model to explain the process underlying flow two and expounding on the types of information that are dissemi nated in this flow. The studies of Hartline and Ferrell (1996) and Hartline, Maxham and McKee manager attitu de (MCSQ) indirectly impacts employee behavior shows the importance of

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42 managerial attitudes on employee outcomes (responses and service delivery), but they do not examine the impact that this managerial attitude toward service quality has on employee attit udes towards service quality. The current study will use their findings as a starting point and expand upon them to show how managerial perceptions of the firm result in employee perceptions of the firm. This study will also delve deeper into the findings of Hartline, Maxham and McKee (2000) that managers are a key part of influencing the dissemination of strategy to customer contact employees. This will be done by showing how manager perceptions of the firm influence employee perceptions of the firm, whic h should result in the shared perception of the importance of technology that will be p assed on to the customer. From this service marketing theory it is possible to create a modified version of the Schramm (1954) model of communication that applies speci fically to the customer. The information source in this model would be the firm, which wants to convey the message of its relationship with technology to customers wh o are the ultimate destination. The firm passes this message to its managers through various vehicles such as memos, training and other points of contact. These managers serve as transmitters of the firm message by encoding it into their own perceptions of technology (MPCAFT). They then transmit this signal to customer contact employees via training, performance requirements, and other controls both formal and informal as defined in Hartline, Maxham and McKee (2000). These customer contact employees then serve as receivers that decode the signal sent by management as their own

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43 (EPCAFT) to the customer (the destination) through service encounters. The feedback loop of the Schramm (1954) model is the response that the customer gives to the firm through behaviors (e.g. changing providers, recommending the firm, complaining, comple menting, etc.). The shared field of experience between the service firm and the customer can be large (i.e., a common culture) or very specific (e.g., the service experience). Because the focus of this paper is on the role that managers play in influencin g employee perceptions, the proposed model only focuses on the path from the transmitter to the receiver. Drawing from the modified Schramm (1954) model described above the following hypothesis is proposed: H1: Manager perceptions of corporate affinity fo r technology positively influence employee perceptions of corporate affinity for technology. The final component of the Schramm (1954) model is noise, which Shannon (1948) defines as anything that causes the signal received by the receiver (the employee) to differ from the signal sent from the transmitter (the manager). The cause of interference is not specified in the Shannon (1948) article, but for the purposes of this revise d model it could come from one of three sources; the manager (transmitter), the employee (receiver) or an outside source. Because this paper focuses on the manager employee interface it will not include the third possible source of noise, but rather will focus on the first two potential sources. On the manager end (transmitter), one k ey source

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44 for technology is clear to the receiver. This is in line with the findi ngs in the field of psychology on implicit attitudes that it is easier for an individual to communicate a message with which he/she agrees than one that goes against his/her personal beliefs. For instance, studies of implied racism (Fazio, Jackson, Dunton and Williams 1995) rely on stimulus about which the respondent has a negative connotation or to categorize a phrase ulus about which that they have a positive about the priming stimulus. That is, if the belief about the target stimulus is incongruous with their own belief about the prime they have received, then it interferes with their ability to communicate the category of the target. This response latency has also been found in a marketing context such as the work of Friese, Wnke and Plessner (2006). They showed that consume rs were likely to revert to implicit attitudes toward products when choice decisions were made under time constraints, and explicit attitudes where there was no pressure to make a choice. From this the following hypothesis is proposed: H2: The personal af finity for technology of the manager moderates the positive relationship between manager perceptions of corporate affinity for technology and employee perceptions of corporate affinity for technology. On the receiver end (employee) the key source of nois personal affinity for technology (EPAFT). How the employee feels about technology will

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45 influence the extent to which he/she accurately interprets the signal from the transmitter. This is inline with the psychological concept of selectiv e attention (Triesman 1969) which states that individuals are only able to process a small portion of the information they receive at any given time and therefore must choose what information to which they are going to focus their attention. In this case if employees have a high personal affinity affinity for technology and thus receive it more clearly than employees with low personal affinity for technology. From this the following hypothesis is proposed: H3: The personal affinity for technology of the employee moderates the positive relationship between manager perceptions of corporate affinity for technology and employee perceptions of corporate affinity for technolo gy. The modified version of the conceptual Schramm (1954) Model can be seen in Figure 2 and all of the hypotheses are graphically depicted in Figure 3.

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46 Figure 2. Conceptual Model Customer Behaviors (Feedback) Firm Field of Experience Customer Field of Experience MPCAFT (Signal) Affinity for Technology (Message) Firm (Information Source) Manager (Transmitter ) Customer Contact Employee (Receiver) Customer (Destination ) EPCAFT (Message ) EPAFT & MPAFT (Noise)

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47 Figure 3. H ypothesized Model Methods Sample In order to test this model a census was conducted of the branch managers and employees from three region s of a large bank that operates in the Southeastern and Eastern United States. The bank industry was selected becau se financial services represent a prototypical service industry that relies on its customer contact employees and has been used in many other studies related to services (e.g., the SERVQUAL scale by Parasuraman, Zeithaml and Berry, 1988). Th ese region s co ntain both rural and urban branches, and are representative of the employee and manager base of the bank according to the Area Vice President s There are 30 managers from different branches and each branch has between three and ten customer contact employe es. A minimum of 15 0 paired comparisons were needed to have enough degrees of freedom for analysis. The surveys and cover letters were distributed to the managers and customer contact employees by the researcher who waited for them to be completed and coll ected them. The response rate for managers was 100% and only 5 employees (all from different Manager Perceived Corporate Affinity for Technology Employee Perceived Corporate Affinity for Technology Manager Personal Affinity for Technology Employee Personal Affinity for Technology + (H 1 ) (H 2 ) (H 3 )

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48 branches) did not return the survey for a response rate of over 96%. Because of such a high response rate, non response bias is of minimal concern. The total numbe r of usable paired comparisons collected was 166, and the demographic profile can be seen in Table 3 for customer contact employees and for managers in Table 4 It is important to note that branch managers were considered customer contact employees because their duties involve them heavily in dealing directly with customers. They were matched with their area manager for paired comparisons.

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49 Table 3. Customer Contact Employee Demographics Age (n=160) Firm Tenure (n = 163) Mean 39.57 Less than 1 year 4.90 % Median 39.5 1 3 Years 33.10% Mode 29 & 35 4 6 years 23.30% 7 9 years 8.60% Gender (n = 166) 10 12 years 9.80% Male 11.40% 13 or more years 20.20% Female 88.60% Industry Tenure (n = 163) Position (n = 166) Less than 1 year 3.70% Bra nch Manager 13.30% 1 5 years 32.50% Assistant Manager 9.00% 6 10 years 19.60% Teller 42.80% 11 15 years 10.40% Financial Service Rep 31.90% 16 20 years 10.40% Investment Advisor 3.00% More than 20 Years 23.30% Other 0.00% Table 4. Manager De mographics Age (n=31) Firm Tenure (n = 31) Mean 43.35 Less than 1 year 0.00% Median 43 1 3 Years 12.90% Mode 42 4 6 years 12.90% 7 9 years 16.10% Gender (n = 31) 10 12 years 9.70% Male 16.10% 13 or more years 48.40% Female 83.90% Indus try Tenure (n = 31) Position (n = 31) Less than 1 year 0.00% Branch Manager 93.50% 1 5 years 6.50% Assistant Manager 0.00% 6 10 years 9.70% Teller 0.00% 11 15 years 12.90% Financial Service Rep 0.00% 16 20 years 22.60% Investment Advisor 0.00% More than 20 Years 48.40% Other* 6.50% *Area Managers

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50 Measures Manager and customer contact employee personal affinity for technology was measured on a ten item scale developed by Edison and Geissler (2003). Each item will be rated on a six point and .909 for managers. Manager and customer contact employee perceptions of corporate affinity for tec hnology were measured on an eight item scale developed by Fleming and Artis (forthcoming). Each item was rated on a six .947 for cust omer contact employees and .938 for managers. Additionally, a measure of fashion consciousness based on the work of Lumpkin and Darden (1982) was included in in this st udy was .760 for customer contact employees and .791 for managers. The manager and employee survey i tems can be seen in Appendix 1 Analysis/Findings The analysis of the data was conducted in several steps. The first step in the analysis process was to s ubject the responses to the personal affinity for technology and perceived corporate affinity for technology scales to a confirmatory factor analysis (CFA). This was necessary this scale was not tested on managers by Fleming and Artis (forthcoming) so the CFA helped to determine if the factor structure also applies to this new population. The personal affinity for technology scale by Edison and Geissler (2003) was tested in a different industry on customers/consumers, so the purpose of the CFA

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51 was to deter mine if the same factor structure held for this different population (i.e., service industry managers and customer contact employees). Because the measures are all single source, self reports, it was also necessary to test for common method bias. This was test, which indicates common method variance if a single factor is found in an unrotated solution or if a first factor explains a majority of the variance in all the measured variables (Podsakof f and Organ 1986). The second method was to take a measure of a completely unrelated construct (e.g., fashion consciousness) and examine its correlation with the constructs of interest. A significant correlation with this marker variable would reveal the presence of common methods bias according to Lindell and Whitney (2001). As can be seen in Table 5 the confirmatory factor analysis raises some red flags about the measurement of the variables. To begin with, the fit statistics of the model are very low. Specifically, none of the major fit indices even approach the traditional minimum of .90 to indicate adequate model fit. Also, the root mean square error of approximation (RMSEA) is well a bove the desired cutoff of .08. Another red flag comes from an exam ination of the inter factor correlations of the latent constructs. Fashion conscientiousness scale was included as a way to check for common method bias because it is not conceptually related to any of the constructs of interest, and thus should not be cor related with them. However, it is correlated with affinity for technology for both managers and customer contact employees. This result is interesting because of common method bias should also result in a correlation between fashion conscientiousness and p erceived corporate affinity for technology, bit that was

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52 not found in either case. This may mean that the correlation is due to another measurement artifact such as response set bias due to the positi on of the scales in the survey. Table 5. Confirmatory Fa ctor Analysis Results Confirmatory Factor Analysis Model Fit Fit Statistic Proposed Saturated Independence Single Const. RMSEA 0.132 N/A 0.221 0.206 Low 90% CI 0.127 N/A 0.217 0.201 High 90% CI 0.137 N/A 0.226 0.211 PCLOSE 0.000 N/A 0.000 0.000 RMR 0.137 0.000 0.389 0.304 GFI 0.595 1.000 0.236 0.293 AGFI 0.545 N/A 0.199 0.220 PGFI 0.530 N/A 0.225 0.265 NFI 0.601 1.000 0.000 0.162 PNFI 0.561 0.000 0.000 0.154 RFI 0.573 N/A 0.000 0.119 IFI 0.670 1.000 0.000 0.181 TLI 0.644 N/A 0.000 0.133 CFI 0.667 1.000 0.000 0.176 PCFI 0.623 0.000 0.000 0.167 ECVI 20.096 10.945 47.888 40.735 Inter factor Covariances and Correlations Latent Constructs Covariance Stnd. Error Sig. Correlation EAFT > EPCAFT 0.000 0.048 0.993 0.006 EAFT > EFC 0.285 0.097 0.003 0.238 EAFT > MAFT 0.097 0.065 0.131 0.080 EAFT > MPCAFT 0.012 0.052 0.818 0.023 EAFT > MFC 0.006 0.075 0.936 0.001 EPCAFT > EFC 0.082 0.060 0.174 0.115 EPCAFT > MAFT 0.028 0.043 0.508 0.026 EPCAFT > MPCAFT 0.017 0.035 0.631 0.056 EP CAFT > MFC 0.083 0.052 0.115 0.119 EFC > MAFT 0.046 0.077 0.550 0.076 EFC > MPCAFT 0.006 0.063 0.925 0.041 EFC > MFC 0.014 0.092 0.877 0.065 MAFT > MPCAFT 0.080 0.047 0.092 0.166 MAFT > MFC 0.313 0.082 0.000 0.295 MPCAFT > MFC 0.087 0.05 6 0.122 0.316

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53 Table 5. Confirmatory Factor Analysis Results (Continued) Confirmatory Factor Analysis Customer Contact Employees Managers Indicator Lambda Theta Sig. Indicator Lambda Theta Sig. Latent Construct: Affinity for Technology EAFT1 1.000 N /A N/A MAFT1 1.000 N/A N/A EAFT2 0.616 0.078 <.001 MAFT2 0.484 0.062 <.001 EAFT3 1.142 0.119 <.001 MAFT3 0.759 0.123 <.001 EAFT4 1.190 0.112 <.001 MAFT4 0.724 0.091 <.001 EAFT5 1.163 0.106 <.001 MAFT5 1.053 0.104 <.001 EAFT6 0.885 0.116 <.001 MAFT6 0. 753 0.107 <.001 EAFT7 1.242 0.105 <.001 MAFT7 1.155 0.094 <.001 EAFT8 1.112 0.098 <.001 MAFT8 1.119 0.090 <.001 EAFT9 1.114 0.113 <.001 MAFT9 0.865 0.107 <.001 EAFT10 1.123 0.115 <.001 MAFT10 1.143 0.110 <.001 Latent Construct: Perceived Corporate Aff inity for Technology EPCAFT1 1.000 N/A N/A MPCAFT1 1.000 N/A N/A EPCAFT2 1.613 0.165 <.001 MPCAFT2 1.431 0.153 <.001 EPCAFT3 1.392 0.148 <.001 MPCAFT3 1.342 0.150 <.001 EPCAFT4 1.609 0.154 <.001 MPCAFT4 1.391 0.130 <.001 EPCAFT5 1.592 0.151 <.001 MPC AFT5 1.350 0.117 <.001 EPCAFT6 1.541 0.163 <.001 MPCAFT6 1.270 0.150 <.001 EPCAFT7 1.785 0.195 <.001 MPCAFT7 1.617 0.184 <.001 EPCAFT8 1.425 0.151 <.001 MPCAFT8 1.267 0.145 <.001 Latent Construct: Fashion Conscientiousness EFC1 1.000 N/A N/A MFC1 1.00 0 N/A N/A EFC2 0.849 0.124 <.001 MFC2 1.008 0.114 <.001 EFC3 1.122 0.162 <.001 MFC3 0.895 0.104 <.001 Despite all that is negative about the proposed factor model, it is still better than any of the baseline comparisons in terms of fit indices and pars imony. An examination of the factor loadings show that all indicators do load significantly on their respective factors. Additionally, the modification indices do not suggest that any of the indicators

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54 should load on other factors. All of the significant m odification indices relate to allowing error variances to correlate, which is inappropriate as this data is not time series in nature. Thus, the proposed factor structure was accepted as the best available and u sed in the subsequent analyses. The second s tep in the analysis process was to use structural equation modeling to examine the relationships between the variables of interest. The first model tested was a full structural equation model in which both the measurement model and the structural paths wer e estimated simultaneously. The first model was a test of the direct effect of manager perceived corporate affinity for technology (MPCAFT), manager personal affinity for technology (MAFT) and employee affinity for technology (EAFT) on employee perceptions of corporate affinity for technology (EPCAFT). This model utilized mean centered data for all measured variables to allow for the creation of single indicator interaction constructs as described by Ping (1996), and he notes that using mean centered data a lso reduces the multicolinearity between the exogenous variables and their interaction terms. As can be seen in Table 6 this model does not fit the data well as evidenced by the poor fit statistics. The low fit indices coupled with the high RMSEA and exp ected cross validation index (ECVI) indicate that there is a better model to explain the data. While some of the fit problems may be attributable to measurement model issues discussed previously, the addition of meaningful structural paths should serve to improve the model fit some. However, in this case the addition of the paths does very little to the model fit which indicates that the expected relationships do not have much explanatory

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55 power. An examination of the statistics related to the structural pat hs in the model confirm this as none of the main effects were significant which means H ypothesis 1 that posits a direct effect of MPCAFT on employee PCAFT is not supported. Additionally without a main effect to moderate, the other two hypotheses become moo t and therefore it does not make sense to run an intercation model. A re examination of the inter factor correlations in the CFA shows that there is no relationship between any of the variables of interest, which further supports the null finding. These su rprising results merited further examination which is discussed below. Table 6. Structural Equation Model Result s Model Fit Fit Statistic Proposed Saturated Independence RMSEA 0.124 N/A 0.236 90% CI Low 0.118 N/A 0.230 90% CI High 0.130 N/A 0.241 RM R 0.133 0.000 0.419 GFI 0.642 1.000 0.231 AGFI 0.597 N/A 0.187 PGFI 0.570 N/A 0.219 NFI 0.673 1.000 0.000 PNFI 0.631 0.000 0.000 RFI 0.651 N/A 0.000 IFI 0.741 1.000 0.000 TLI 0.722 N/A 0.000 CFI 0.739 1.000 0.000 PCFI 0.694 0.000 0.000 ECVI 13.5 97 8.037 39.207 Structural Paths Path Weight Sig. MPCAFT > EPCAFT 0.029 0.703 MAFT > EPCAFT 0.041 0.516 EAFT > EPCAFT 0.005 0.926

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56 To explore the possibility that measurement issues were masking some of the expected relationships, a series of s tepwise regressions that brought in each path of the value) or explanatory power (adjusted r square). Again, mean centered data was used to reduce multicolinearity between pre dictor variables and their interactions. This mean centered data was then used to create average scale scores by summating the indicators of each construct and dividing by the number of indicators. These scale scores were then used to create interaction te rms by multiplying them together. The summated scores were used as the predictor and criterion variables in the regression model. As can be seen in Table 7 none of the main effects (MPCAFT, MAFT and EAFT) or interactions were significant predictors of emp loyee perceptions of corporate affinity for technology. These findings corroborate the SEM findings and indicate that the expected relationships are not present in this data. These findings are discussed more below. Table 7. Nested Regression Results Mo del 1 Model 2 Model 3 Model 4 Model 5 Path Beta Sig Beta Sig Beta Sig Beta Sig Beta Sig MPCAFT > EPCAFT 0.056 0.72 0.054 0.50 0.053 0.50 0.034 0.67 0.011 0.89 MAFT > EPCAFT N/A N/A 0.017 0.8 3 0.017 0.83 0.006 0.94 0.005 0.95 EAFT > EPCAFT N/A N/A N/A N/A 0.003 0.97 0.008 0.92 0.002 0.98 MPCAFTxMAF > EPCAFT N/A N/A N/A N/A N/A N/A 0.105 0.20 0.109 0.19 MPCAFTxEAFT > EPCAFT N/A N/A N/A N/A N/A N/A N/A N/A 0.109 0.18 Model F value 0.523 0.47 0.284 0.75 0.189 0.90 0.551 0. 70 0.812 0.54 Nested F value N/A N/A 0.048 0.83 0.002 0.97 1.635 0.20 1.843 0.18 Adjusted R square 0.003 0.009 0.015 0.011 0.006

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57 Discussion Based on the findings above, none of the hypotheses were supported. While this is hard to understand conceptually and logically some of the previously mentions shortcomings of the study may explain why this null result occurred. The first reason for this surprising result may be due to measurement error; specifically, the high correlation between affinity for technology and fashi on conscientiousness for both managers and customer contact employees raises the question of the validity of the measures in this study and hint at some sort of measurement artifact (most likely response set bias) creating some of the unexpected results. A second possible culprit for these non significant findings could be the fact the all of the observed variables were not normally distributed and many showed a heavy negative skew. This may be interfering with the discovery of relations by restricting the variance in either the predictor or criterion variables. A third plausible explanation for the lack of results is an inadequate sample size. While there are 166 matched pairs, they only represent 31 managers, and this may be constraining the variance in t he predictor variables making it impossible to find the expected relationships. It may be necessary to collect a new data set that includes more managers (at least 100) and limits the matched pairs per manager to a smaller number (no more than 3) to have a n accurate picture of the nature of the relationship. Implications Academic While the hypotheses in this study were not supported, it still has several important implications for academicians. The first is that this paper explores the

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58 role of technology in the employee firm interface. As a recent call for papers in the Journal of Professional Selling and Sales Management points out, this type of study is important for understanding the role of technology in the firm sales force interface, or in this case service delivery, and has been neglected in the literature to this point. The next important academic implication is that this study begins the process of developing the nomological network for perceived corporate affinity for technology. As a new constr uct it is necessary to develop and test theoretically driven hypotheses to determine if this construct has nomological validity. On a related note, this paper also engages in a more rigorous test of the validity and reliability of the personal affinity for technology scale (Edison and Geissler 2003) than the original paper in which it was developed. Subjecting it to a confirmatory factor analysis and testing it on different populations to ascertain its generalizability accomplishes these important tests of the scale. The third major academic implication of this paper comes from the application of a well known theory to a new area of study. This paper applies the communication model of S are shared with employees to form customer contact employee perceptions of firm attitudes. Additionally, the use of this model allows for an explanation of the factors that interfere with the clear transmission of these messages especially between the por tions of the channel under the control of the firm (managers and employees). This is important as it draws in a model from other areas into the study of services marketing and serves as a theoretical reference point for future research into the how interna l marketing

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59 communication occurs and the potential threats to the clear transmission of messages between the firm and customer. Managerial This study contains several potential benefits for managers if the hypotheses can be supported in the future. The f irst benefit is that this study shows the importance of managers in the process of sharing information with employees. However, this information is not just what the firm expects from employees in terms of performance and activities as shown in past studie s, but also information about the attitudes that the firm has towards objects or causes. This is vital as firm attitudes towards causes, such as the environment, are believed to be vital to increasing patronage. One example of firms engaging in behaviors t hat project a favorable attitude toward a cause is the investment in environmentally friendly initiatives such as Wal 2006). Additionally, firm attitudes towards technology, as communicated by frontline employees, should influence customer (Woodall, Colby and Parasuraman 2007); and as noted by Honebein and Cammarano (2006) properly implemented technologies can be a cost savings for firms as well as a patrons. Another of the key benefits of this study for managers is that it emphasizes the importance of the role of managers i n sharing information with employees about the firm goes beyond just telling employees what the company expects. Specifically, it identifies that how the manager personally feels about the message they are sending influences the

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60 signal that the employee re ceives and in turn the message that is passed on to the customer. Thus, managers must be cognizant of their own feelings in regards to technology, or other objects/causes, when sharing firm feelings about technology (or other objects/causes) with their cus tomer contact employees and be mindful of the impact of their personal attitudes on t he message they are delivering. A final benefit of this study for managers is that it highlights the importance of employee personal attitudes on the reception of communic ations about firm attitudes are received. This is relevant, as according to the internal marketing literature, these perceptions of the firm are then transmitted to the customer and can influence serv ice delivery perceptions ( Lai 2006). Thus, managers nee d to be aware of how their relationship with technology to the employee, and may need to spend more time communicating the message to those employees whose personal attitudes are not in line with the message that the firm is trying to convey to customers.

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61 References Belch, G. E. & Belch M. A. (2006) Advertising and p romotion: An i ntegrated m arketing c ommunications p erspective 7 th E d ition. New York, NY: McGraw Hill/Irwin Berry L. L. (1981) The e mployee as c ustomer Journal of Retail Banking 3 (1), 33 40. Churchill, G. A. (1979) A p aradigm for d eveloping b etter m easures of m arketing c onstructs Journal of Marketing Research 16 (1), 64 73. Curran, J. M. & Meuter M. L. (2005) Self S ervice t echnology s doption: Comparing t hree t echnologies Journal of Services Marketing 19 (2), 103 113. Edison, S. W. & Geissler G. L. (2003), Measuring a ttitudes t owards g eneral t echnology: Antecedents, h ypotheses and s cale d evelopment Journal of Targeting, Measurement and Analysis for Marketing 12 (2), 137 156. Fazio, R. H., Jackson, J. R., Dunton B. C., & Williams C. J. (1995) Variability in automatic activation as an unobtrusive measure of racial attitudes: A bona fid e pipeline? J ournal of Personality and Social Psychology 69 (6), 1 013 1 027. Fleming, D. and Artis A. B. ( forthcoming ). Measuring c orporate a ffinity for t echnology: A s cale for c ustomers and e mployees Journal of Personal Selling and Sales Management Special Issu e lling: Threats and Friese, M. Wnke M., & Plessner H. (2006) Implicit c onsumer p references and t heir i nfluence on p roduct c hoice Psychology & Marketing 23 (9), 727 740. Geissler, G L. & Edison S. W. (2005) Market m a ttitudes t owards g eneral t echnology: Implications for m arketing c ommunications Journal of Marketing Communications 11 (2), 73 94. Geo rge, W. R. (1990) Internal m arketing and o rganizational b ehavior: A p artnership in d eveloping c ustomer c onscious e mployees at e very l evel Journal of Business Research 20 (1), 63 70.

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62 Goldman, R. D., Platt B. B., & Kaplan R. B. (1973) Dimensions of a ttitudes t oward t echnology Journal of Applied Psychology 57 (2), 184 187. Gronroos, C (1984). A s ervic e q uality m odel and i ts m arketing i mplications European Journal of Marketing 18 (4), 36 44. Gunther, M. (2006) http://money.cnn.com/2006/07/25/news/companies/wal mart short.fortune/ Harris, E. G. & Fleming D. E. ( 2005). Assessing the h uman e lement in s ervice p ersonality f ormation: Personality c ongruency and the f ive f actor m odel Journal of Services Marketing 19 (4), 187 198. Hartline, M. D. & Ferrel O. C. (1996). The m anagement of c ustomer c ontact s ervice e mploy ees: An e mpirical i nvestigation Journal of Marketing 60 (4), 52 70. Hartline, M. D., Maxham III J. G., & McKee D. O. (2000). Corr i dors of i nfluence in the d issemination of c ustomer o riented s trategy to c ustomer c ontact s ervice e mployees Journal of Ma rketing 64 (2), 35 50. Heinssen, R. K., Glass C. R., & Knight L. A. (1987) Assessing c omputer a nxiety: Development and v alidation of the c omputer a nxiety r ating s cale Computers in Human Behavior 3 (1), 49 59. Honebein P C. & Cammarano R. F. (2006) Customers at w ork Marketing Management 15 (1), 26 31. Jarvis, C B. MacKenzie S. B., & Podsakoff P. M. (2003) A c ritical r eview of c onstruct i ndicators and m easurement m odel m isspecification in m arketing and c onsumer r esearch Journal of Consumer R esearch 30 (2), 199 218. Johnson, R. (2008) Internal s ervice Barriers, f lows and a ssessment International Journal of Service Industry Management 19 (2), 210 231. Lai, J. (2006) Assessment of e p erceptions of s ervice q uality and s atisfaction with e b usiness International Journal of Human Computer Studies 64 (9), 926 938. Lindell, M K. & Whitney D. J. (2001) Accounting for c ommon m ethod v ariance in c ross s ectional r esearch d esigns The Journal of Applied Psychology 86 (1), 114 121. Lumpkin, J R. & Darden W. R. (1982). Relating t elevision p reference v iewing to s hopping o rientations, l ife s tyles an d d emographics: The e xamination of p erceptual and p reference d imensions of t elevision programming, Journal of Advertising 11( 4 ), 56 67.

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63 Lusch, R. F., Boyt T. & Schuler D. (1996) Employees as customers: The role of social controls and employee socializ ation in de veloping patronage. Journal of Business Research 35 (3), 179 187. MacKenzie, S. B. (2003) The d angers of p oor c onstruct c onceptualization Journal of the Academy of Marketing Science 31 (3), 323 326. Parasuraman, A. (2000). Technology r eadine ss i ndex (TRI): A m ultiple i tem s cale to m easure r eadiness to e mbrace n ew t echnologies Journal of Service Research 2(4), 307 320. Parasuraman, A., Zeithaml V. A., & Berry L. L. (1988) SERVQUAL: A m ultiple item s cale for m easuring c onsumer p erceptions of s ervice q uality Journal of Retailing 64 (1), 13 40. Ping, R A. (1996) Latent v ariable r egression: A t echnique for e stimating i nteraction and q uadratic c oefficients Multivariate Behavioral Research 31 (1), 95 120. Podsakoff, P. M. & Organ D. W. ( 1986) Self Reports in o rganizational r esearch: Problems and p rospects Journal of Management 12 (4), 531 544. Rossiter, J. R. (2003) The C OAR SE procedure for scale development in marketing International Journal of Research in Marketing 19 (4), 305 33 5. Schramm, W. L. (1954) The process and effects of mass communication Urbana, IL: University of Illinois Press. Segars, A. H. (1997). Assessing the u nidimensionality of m easurement: A p aradigm and i llustr ation within the c ontext of i nformation s ystems r esearch Omega 25 (1), 107 121. Shannon, C. E. (1948) A m athematical t heory of c ommunication Bell System Technical Journal 27(July & October), 379 423 & 623 656. Shannon C. E. & Weaver W. (1963) The m athematical t heory of c ommunication Urbana, IL: University of Illinois Press. Solomon, M. R., Cornell, L. D., & Nizan A. (2009). Launch! Advertising and Promotion in Real Time Nyack, NY: Flat World Knowledge, LLC. Treisman, A. M. (1969) Strategies a nd m odels of s elective a ttention Psychological Review 76 (3), 282 299. Wasmer, D. J. & Bruner, III G. C. (1991) Using o rganizational c ulture to d esign i nternal m arketing s trategies The Journal of Services Marketing 5 (1), 35.

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64 Woodall, R. D., Colby C L., & Parasuraman A. (2007) E volution to r evolution: Capitalize on the i mminent e ra of e xplosive e s ervices g rowth Marketing Management 16(2), 29 34. Zerbe, W. J., Dobni D., & Harel G. H. (1998) Promoting e mployee s ervice behavio r: The r ole of p erceptions of h uman r esource m anagement p ractices and s ervice c ulture Canadian Journal of Administrative Sciences 15 (2), 165 179.

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65 Appendix 1 Manager and Customer Contact Employee Survey Items Affinity for Technology (Edison and Geissler 2003) '7' = Exactly like me) 1. I enjoy learning new computer programs and hearing about new technologies 2. If I am given an assignment that requires that I learn to use a n ew program or how to use a machine, I usually succeed. 3. Solving technological problems seems like a fun challenge 4. Technology is my friend 5. I find most technology easy to learn hem down 7. I relate well to technology and machines 8. I am comfortable learning new technology 9. I know how to deal with technological malfunctions or problems 10. I feel as up to date on technology as my peers. Perceived Corporate Affinity for Tec hnology (Pretest) = Strongly Disagree, '7' = Strongly Agree) 1. My company views technology as a friend 2. My company offers the latest technologies 3. I expect my company 4. My company seems comfortable implementing new technology 5. My company relates well to technology 6. My company knows how to deal with technological problems 7. I feel my company is as up to date on technology as its competitors 8. My company shows its relationship with technology by offering secure technology based services Fashion Consciousness (Lumpkin and Darden 1982) Please rate your agreement with how well the following statement s describe you ( = Strongly

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66 When I must choose between the two, I usually dress for fashion, not for comfort An important part of my life and activities is dressing smartly A person should try to dress in style Demo graphics Please circle the answer that best describes you for each question below. What is your age? Under 20 20 29 30 39 40 49 50 59 60 or older What is your gender? ______________ How long have you worked at Bank X ? Less than 1 year 1 3 Years 4 6 years 7 9 years 10 12 years 13 or more years What is your current position at Bank X ? Branch Manager Assistant Manager Teller Financial Service Rep Investment Advisor Other ___________ How long have you worked in the banking industry? Less than 1 year 1 5 years 6 10 years 11 15 years 16 20 years More than 20 years Branch _________________

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67 Chapter Three: Developing the Nomological Network of Perceived Corporate Affinity For Technology: Study 2 Employee P erceptions and I ts E ffect on T heir U se of S elf D irected L earning Sales and service personnel with superior expertise provide a firm with a competitive advantage because customers rely on the knowledge base of these service personnel when making decisions. Self directed lear ning is one way that these boundary spanning employees can improve their knowledge base. The models of self directed learning in sales have offered a new area for research, but all of the extant models lack a key component of technology and have not been e xtended to the services arena. This paper develops and tests a model that extends the work of Artis and Harris (2007) by showing how the personal affinity for technology and perceptions of firm affinity toward technology of these boundary spanning employee s influence their use of self directed learning projects to develop professional expertise. Introduction The ability of salespeople to learn it is at the heart of many key concepts in the sales literature because of the growing need for salespeople to be able to adapt to rapidly changing competitive environments, customer needs and regulatory and firm requirements ( Jones, Brown, Zoltners and Weitz 2005; Marshall, Moncrief and Lassk 1999). Most sales force research on learning has focused on formal trai ning (Lupton,

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68 Weiss and Peterson 1999; Cron, Marshall, Singh, Spiro and Sujan 2005) or learning through experience (Turley and Geiger 2006; Sujan, Weitz and Kumar 1994). While these are important ways for salespeople and service personnel to learn, they may not be marketplace. Recently, the concept of self directed learning been incorporated into the sales area (Hurley 2002; Artis and Harris 2007) from the adult e ducation field. Self directed learning provides a new insight into salesperson learning by looking at how employees can be responsible for their own learning, implementing that learning to reach their personal and corporate goals and evaluating the outcome s of their learning (Knowles 1975). Currently, however, the application of self directed learning in the sales literature has been mainly conceptual (Artis and Harris 2007) in nature without any empirical testing. Also, although Artis and Harris offer an insightful and extensive model, it lacks a factor that may play an important part explaining salesperson use of self directed learning projects, namely technology. Specifically, their framework fails to account for the influence of how the individual rela tes to technology (personal affinity for technology) elf directed learning projects. Additionally, the work i n this area has exclusively focused on traditional sales forces, while excluding service industry customer contact employees who engage in selling type behaviors. As noted by Harris and Fleming (2005), service personnel play a vital role in consumer percep tions of service outcomes just as salespeople are vital to the

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69 client satisfaction (Goff Boles, Bellenger and Stojack 1997), while the work of Hurley (1998) looked at the importance service orientation in the success of both service personnel and sales people. The reason these two types of positions are similar in terms of their changing environments and their need for learning is the fact that they are boundary spanning positions that involve a great deal of direct customer contact (Singh and Rhodes 19 91). Thus, the purpose of this paper is to extend the conceptual work of Artis and Harris (2007). The first extension is to empirically demonstrate the importance of salesperson and service personnel (employee) affinity for technology and employee percepti on of corporate affinity for technology in the use of the various types of self directed learning projects. The second extension is to apply their framework beyond traditional sales force into the services area, thus showing that the nomological network ar ound self directed learning is important across various types of boundary spanning positions. Literature Review Self d irected l earning Self directed learing has a long history in the adult education literature (see Ellinger 2004 for a historical overvi ew of the topic). As Ellinger (2004) notes, self directed learning has been defined in a variety of way over the years, but the definition provided by Knowles (1975) seems to have the most applicability to the sales domain. His definition contains 8 keys t hat distinguish self directed learning : (1) it is a process (2) that is initiated by the individual, (3) which may or may not involve the help of others, to (4) identify their learning needs, (5) develop learning goals from these

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70 needs, (6) find the necess ary resources to attain these goals, (7) select and implement the proper learning strategies to meet their goals and (8) determine how to measure learning outcomes. Most of the research in self directed learning has used the self directed learning project (SDLP) as conceptualized by Tough (1967) for the unit of analysis. Tough defined a self directed learning projects along the lines of 4 characteristics of the learning event so that it is : (1) deliberate, (2) related activities that (3) take up at least 7 hours in a six month time frame and (4) generates specific knowledge, skills or la sting change in the individual. Clardy (2000) extended the conceptual thought on self directed learning projects by developing a typology of four different types of projects based on who plays a key role in initiating the project and the nature of the learning involved. The first type of self directed learning project he identified is called an induced project, which is a learning project that is required either by the firm or other regulatory body. These projects are most useful when the individual is unaware of what they need to know, where to find the information, or how to assess their learning. Usually, the organization or individual requiring this learning provides the e mployee with the information and also assess whether the information was learned (Artis and Harris 2007). An example of this type if SDLP would be the traditional training that organi zations provide to salespeople. The second type of SDLP identified by Cl ardy (2000) is called synergistic, and learning is provided by the organization or other stakeholder, but the individual has the option of whether or not to engage in the SDLP, and the learning is not assessed by anyone other than the individual. This type of SDLP is particularly useful when the

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71 individual is aware of what they need to know, but do not know how to find the needed information. Artis and Harris (2007) cit e organizational libraries as an example of the organization providing the inf ormation for this type of SDLP. The third type of SDLP Clardy (2000) identifies is called voluntary. These projects are almost entirely enacted by the individual, and happen when the individual knows what knowledge is needed, where to find the necessary information and how to evaluate when they have learned it. An example of this type of SDLP would be for a salesperson to decide that they need to learn about finance to better unde rstand the leasing terms that their company offers to be better able to discuss these options with prospects, and then goes about f inding sources of information. The fourth type of SDLP Clardy (2000) identified is called scanning. This type of project is a n ongoing project with no set end, which differentiates it from any of the other types; but otherwise it is very similar to a voluntary SDLP in that the individual knows what knowledge is needed, where to find the necessary information and how to evaluate when they have learned it. An example of this type of SDLP would be the salesperson clients why they should purchase his offerings. This typology serves as a guide for our conceptualization of the dependent variables in our examination of how personal and perceived firm feelings toward technology influence the use of self directed learning. Artis and Harris (2007) extended the notion of SDLPs into the sales area by provi ding a conceptual model of the antecedents, moderators, mediators and outcomes of the use of SDLPs by salespeople. Through their detailed review of the self directed learning literature they propose four antecedents, two moderators and one mediator of the

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72 use of SDLPs by salespeople. The four individual characteristics they identified as antecedents are learner self directedness, confidence in self directed learning skills, contextual understanding and motivation to learn. The two moderators they propose ar e environmental turbulence and organizational learning climate. The moderating variable proposed is willingness to use SDLPs. This model serves as a basic framework that guides our conceptualization of how technology influences the use of SDLPs. Affinity for technology Edison and Geissler (2003) developed the construct of positive affect toward technology (in general) attitude people hold toward technology. They fo und several antecedents of affinity for technology including optimism, need for cognition, self efficacy, age, and gender. Geissler and Edison (2005) is the only other published study to utilize this scale. They found that affinity for technology was posit ively related to market mavenism. In addition, this study repeated both the exploratory factor analytic and confirmatory factor analytic techniques used in their first study with similar results. This indicates that the factor structure of this construct h which is very close to the .89 they found in the original scale development study. While there have been other scales and studies of how individuals interact with technology (Gold man, Platt and Kaplan 1973; Heinssen, Glass and Knigh t 1987; Parasuraman 2000), the Edison and Geissler (2003) conceptualization best fits within the service and SDLP context for two reasons. First, their study is concerned with the affect people have adopt the technology (Parasuraman 2000) or the underlying factors that lead to the use of

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73 technology (Goldman, Platt and Kaplan 1973). Second, the Edison and Geissler (2003) work defines technology much more generally than other studies, which tend to focus on technology as computers, the Internet, or other specific technological tools (Heinssen, Glass and Knight 1987). These unique characteristics of the affinity for techno logy scale are important as how service personnel feel about technology (their affect) should be a much more important determinant in their use of SDLP than if they are ready to adopt a technology (i.e., purchase it). Also, the technologies used in SDLPs m ay be things other aud iotapes to learn new languages. A proposed extension of the affinity for technology construct is that of customer and employee perceptions of corpora te affinity for technology (Fleming and Artis forthcoming) the perception individuals have of the affect (p. 8) This construct places emphasis on is very different from how the individual feels about technology which is what is was developed and validated by Fleming and Artis (forthcoming) using both qualitative and quantitative methods based on well respected scale development procedures (Churchill 1979; Jarvis, MacKenzie and Podsakoff 2003; MacKenzie 2003; Rossiter 2003; Segars 1997). According to t he ir qualitative study, these perceptions of corporate affinity for technology can be derived from many different points of contact with an organization such as advertisements, encounters with employees and contact with managers. The ir quantitative studie s via exploratory and confirmatory factor analys e s

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74 found that the same eight item solution created the best factor structure for both customers and employees, and a correlational analysis found that from a customer perspective this construct is related to both personal affinity for technology (r=.34) and perceptions of service performance (r=.69) While their work did not test the relationships between employee perceptions of corporate affinity for technology and other variables, the customer findings indic ate that how an individual perceives a firm relating to technology does impact how they perceive the firm in other areas. This is important in the service setting because what the customer contact employee thinks the firm as valu es will influence both the methods they use and how they communicate with current and prospective clients. Model Development This paper develops a model of the role that personal and perceptions of firm affinity for technology play in the use of the various self directed learning projects in the typology of Clardy (2000) by drawing on the model of Artis and Harris (2007). In their model, they identify the four key antecedents of SDLP use as learner self directedness, confidence in self directed learning skills, contextual understa nding and motivation to learn. Employee affinity for technology is an individual level characteristic, and is Geissler (2003). Consistent with the Artis and Harris (2007) mo del this characteristic should be related to the use of self directed learning projects; specifically, it should influence the antecedent characteristics of confidence in self directed learning skills and contextual understanding. In both of these cases te chnology can serve as a means to

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75 improve these antecedent characteristics because having a positive affect toward technology will help by aiding the employee in developing information literacy Information literacy is defined in the library science literat ure as an individual who knows when they have a need for information, identif ies the information needed to address a given problem or issue can find needed information can evaluat e the information is able to organ ize the information and is able to us e the information effectively to address the problem or issue at han d (Breivik 2005) Tradtionally, information literacy has focused on print media, but recently it has also spread to information technology as well (Mackey 2005) where the it has been shown that information literacy plays a role in the effective use of information technology, although the exact nature of the relationship in not clear. As noted above, SDLPs require that the individual recognize what they need to know and how to access this i nformation, and information literacy is a way to accomplish both of these parts of the projects. The individual is going to hav e to engage in search behaviors; that is they are going to have to be information literate to thrive in a rapidly changing enviro nment. T he use of technology will improve the efficiency of employee search behaviors or confirm what they think that they know about the topic of interest and the sources they should use to learn about it. Additionally, the use of technology to expedite t hese searches is vital in rapidly changing environments ( i.e., the sales and services domains) as it allows the individual to gather the most current information as it is available and apply it before it becomes out dated. Therefore, having a high affinit y for technology would make it easier to engage in voluntary and scanning types of SDLPs. Th erefore the following hypothesis is proposed :

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76 H 1 : P ersonal affinity for technology is positively related to employee use of self directed learning projects. In their model, Artis and Harris (2007) identify organizational climate factors as a moderator of the relationships between individual characteristics and the use of SDLPs. While service personnel perception of corporate affinity for technology is not a clima te variable per se, it shares many aspects with climate factors. The major difference is that perceived corporate affinity for technology is interested in how individuals see the anifestation of a culture that is shared. However, much climate research has avoided the shared aspect of climate variables in favor of individual perceptions of the climate (Neal and Griffin 2006). Additionally, it makes sense that how the individual per ceives the corporate affinity for technology should influence the relationship between their own affinity for technology and their use of certain types of SDLPs. This is tied to the antecedent condition of motivation to learn in the Artis and Harris (2007) model. That is, if the employee feels that the firm will appreciate their use of technology in the learning process, then they will be more likely to use technology to assist in their SDLPs, but if they do not feel that they will be rewarded for the use o f technology they will not be as likely to use technology as part of SDLPs. For instance, if an individual is high on affinity for technology, the previous hypothesis states that they will be more likely to engage in SDLPs. However, if they feel that the f irm has a low affinity for technology it should reduce the strength of this relationship because the employee will not feel that

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77 using technology as a basis for these SDLPs will be appreciated The opposite effect would be expected to occur if the individua l perceived high levels of corporate affinity for technology. Also, if the employee has low affinity for technology, they will be more likely to use technology if they feel they will be rewarded for it some way because the firm has a high affinity for tech nology. Based on this reasoning and the model by Artis and Harris (2007) the following hypothesis is proposed: H 2 : Perceived corporate affinity for technology will moderate the relationship between personal affinity for technology and employee use of self directed learning projects. Alternatively, service personnel perception of corporate affinity for technology may exert a direct effect on the propensity of the individual engage in self directed learning projects. This is because the perception of an env ironment that encourages the use of technology may lead the individual to utilize technology whenever it is appropriate. The idea that a perceived climate may lead d irectly to employee behavior is also found in the organizational psychology literature. Spe cifically, the relationship between safety climate and safety behavior has been demonstrated (Neal and Griffin 2006). The relationship is based on the notion of social exchange theory (Thibaut and Kelley 1959), which states that relationships involve a m utual give and take between the two parties involved. If the employee believes that the firm is provides something for the employee, then the employee will reciprocate to the firm by taking advantage of this opportunity. In this case, if the firm shows an affinity for technology, then the employee

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78 will utilize the available technology for the betterment of the firm ( i.e., learning). Thus, the following hypothesis is proposed: H3: Perceived corporate affinity for technology is positively related to employee use of self directed learning projects. These hypotheses can be seen graphically in Figure 4 Figure 4. Empirical Model Method Sample In order to test this model a sample of 1 99 boundary spann ing employees was collected These participants were drawn from a census of customer contact employees in 30 branches in three region s of a large bank that operates in the Southeastern and Eastern United States. Th ese region s were selected because they include both urban and rural b ranches that are representative of the overall employee base of the bank according to one Area Vice President. The bank has over $170 billion in assets and was selected as the financial services industry represents a prototypical service industry that reli es on the selling skills of its boundary spanning employees and has been Salesperson Affinity for Technology Use of Self Directed Learning Projects Perceived Corporate Affinity for Technology + (H1) (H2) + (H3)

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79 used in many other studies related to services (e.g., the SERVQUAL scale by Parasuraman, Zeithaml and Berry, 1988). The surveys and cover letters were distributed to the employees by a researcher. The researcher waited until the surveys were completed and collected them from the employees. The response rate for most branches was 100% and the most employees missing from a single branch was one which occurred in five cases. This minimiz es concerns about non response bias. A demographic breakdown of the respondents can be seen in Table 8 Table 8. Demographics Age (n=193) Firm Tenure (n = 196) Mean 39.68 Less than 1 year 5.60% Median 40 1 3 Years 32.10% Mode 35 4 6 years 20.40% 7 9 years 10.20% Gender (n = 199) 10 12 years 9.70% Male 12.10% 13 or more years 22.00% Female 87.90% Industry Tenure (n = 196) Position (n = 199) Less than 1 year 3.60% Branch Manager 15.10% 1 5 years 31.60% Assistant Manager 9.00% 6 1 0 years 18.90% Teller 39.70% 11 15 years 10.70% Financial Service Rep 32.20% 16 20 years 10.70% Investment Advisor 2.50% More than 20 Years 24.50% Other 1.50% Measures Service personnel affinity for technology was measured on a ten item scale developed by Edison and Geissler (2003). Each item rated on a six point Likert in this study. Service personnel perception of corporate affinity for technology was m easured on an eight item scale developed by Fleming and Artis (forthcoming). Each item was rated on a six

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80 voluntary and scanning self directed learning projects were measured by providing the definition and example of each and asking the employee how many of these projects she/he has engaged in over the past six months. Additionally, a measure of fashion cons ciousness based on the work of Lumpkin and Darden (1982) was included in the survey to enable a test for common method bias. This scales was also measured on a six alpha was .781 in this study. These items can be seen in Appendix 2 Analysis/Findings Confirmatory f actor a nalysis The first step in the analysis process was to subject the responses to the personal affinity for technology and perceived corporate affinity f or technology scales to a confirmatory factor analysis (CFA). This was necessary as the personal affinity for technology scale by Edison and Geissler (2003) was tested in a different industry on customers/consumers, and the Fleming and Artis (forthcoming) perceived corporate affinity for technology scale has only been tested via confirmatory analysis in one study. The purpose of the CFA was to determine if the same factor structure held for this different population (i.e., service industry employees) in the case of the personal affinity for technology (PCAFT) scale and to provide an additional test of the factor structure in the case of the perceived corporate affinity for technology (AFT) scale. Because the measures are all single source, self reports, it w as also necessary to test for common method bias. This was done in two ways. The first was to test, which indicates common method variance if a single

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81 factor is found in an unrotated solution or if a first factor explains a major ity of the variance in all the measured variables (Podsakoff and Organ 1986). The second method was to take a measure of a completely unrelated construct (e.g., fashion consciousness) and examine its correlation with the constructs of interest. A signific ant correlation with this marker variable purports reveal the presence of common methods bias according to Lindell and Whitney (2001). As can be seen in T able 9 the confirmatory factor analysis shows moderate fit based on the root mean square error of ap proximate RMSEA of .076, Tucker Lewis index (TLI) of .924, comparative fit index (CFI) of .932 and estimated cross validation index (ECVI) of 2.478. The other fit indices infer that the model is not the optimal way to model the data, but a comparison of th e proposed factor structure with the saturate, independence and single construct baseline models shows that it does a much better job of explaining the data than the independence (minimalist) model and the single construct model while being much more logic al and parsimonious than the saturated model. Given all of the information, it appears that the proposed factor structure does a reasonably good job of explaining the data. One reason for the lack of a better fit from the proposed factor structure may stem from the fact that the observed variables are not normally distributed (in fact most show a significant negative skewedness) and this deviation from normality adversely impacts the maximum likelihood estimation technique used to fit this model. An exami nation of the factor loading further supports the use of the proposed factor structure as all indicators loaded significantly on the expected latent construct. Furthermore, a review of the modification indices does not show that that any of the indicators cross load onto any of the other latent constructs, which give further support to

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82 the factor structure. Looking at the inter factor correlations, however, does raise some concerns. The significant correlations between PCAFT and fashion conscientiousness (F C), and AFT and FC raise the possibility of some kind of measurement artifact that is clouding the results of the factor analysis. A closer look does mitigate the concern about the PCAFT/FC link as the magnitude is small and the significant result is mostl y due to the large sample size. On the other hand, the AFT/FC relationship does cause concern because of its magnitude. The reason for this relationship is not clear as it could be the result of common method bias, some other measurement artifact like a re sponse set, or an unidentified third variable. Table 9. Confirmatory Factor Analysis Results Confirmatory Factor Analysis Indicator Lambda Theta Sig. Stdzd Weight Latent Construct: Affinity for Technology AFT1 1.000 N/A N/A 0.683 AFT2 0.697 0.080 <. 001 0.658 AFT3 1.232 0.126 <.001 0.734 AFT4 1.241 0.116 <.001 0.820 AFT5 1.216 0.110 <.001 0.847 AFT6 0.986 0.120 <.001 0.615 AFT7 1.351 0.114 <.001 0.914 AFT8 1.240 0.106 <.001 0.901 AFT9 1.259 0.122 <.001 0.783 AFT10 1.238 0.122 <.001 0.771 Late nt Construct: Perceived Corporate Affinity for Technology PCAFT1 1.000 N/A N/A 0.627 PCAFT2 1.647 0.170 <.001 0.841 PCAFT3 1.510 0.154 <.001 0.837 PCAFT4 1.654 0.156 <.001 0.933 PCAFT5 1.664 0.156 <.001 0.947 PCAFT6 1.651 0.172 <.001 0.810 PCAFT7 1 .909 0.202 <.001 0.794 PCAFT8 1.527 0.162 <.001 0.795 Latent Construct: Fashion Conscientiousness FC1 1.000 N/A N/A 0.694 FC2 0.845 0.103 <.001 0.714 FC3 1.075 0.130 <.001 0.820

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83 Confirmatory Factor Analysis Inter factor Covariances and Correlations Latent Constructs Covariance Stnd. Error Sig. Correlation AFT > PCAFT 0.007 0.039 0.851 0.014 AFT > FC 0.277 0.085 0.001 0.292 PCAFT > FC 0.099 0.055 0.075 0.149 Model Fit Fit Statistic Proposed Saturated Independence Single Const. RMSEA 0. 076 N/A 0.276 0.216 Low 90% CI 0.066 N/A 0.268 0.207 High 90% CI 0.087 N/A 0.285 0.224 PCLOSE 0.000 N/A 0.000 0.000 RMR 0.106 0.000 0.589 0.356 GFI 0.843 1.000 0.242 0.400 AGFI 0.805 N/A 0.166 0.266 PGFI 0.679 N/A 0.220 0.327 NFI 0.882 1.000 0.000 0.430 PNFI 0.781 0.000 0.000 0.387 RFI 0.866 N/A 0.000 0.367 IFI 0.933 1.000 0.000 0.455 TLI 0.924 N/A 0.000 0.391 CFI 0.932 1.000 0.000 0.452 PCFI 0.826 0.000 0.000 0.407 ECVI 2.478 2.333 16.380 10.173 Structural e quation m odels The second step in the analysis process w as to fit a series of full structural equation models to test the hypotheses. Th ese model s in clude both the measurement model and the structural paths were conducted in two parts T he first model was a test of the direct effect of both perceived corporate affinity for technology and personal affinity for technology on the use of the four types of self directed learning projects (SDLP s ) and the second wa s a test of the moderating effect of personal affinity for technology on the rel ationship between perceived corporate affinity for technology and the use of the various types of SDLPs. It is important to note that all exogenous variables were mean centered in order to reduce multicolinearity when the interaction is

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84 included in the mod el (Ping 1996). The moderating effect was estimated using the method described by Ping (1996) for dealing with continuous variable interactions in and PCAFT are multipl ied and entered it into the model as a single indicator latent construct with a fixed factor loading and error variance based on the loadings and variance s of the non interaction model. The results of the structural equation modeling can be seen in Table 10 A n examination of the model fit statistics for each type of self directed learning project show that either the interaction model or the non interaction model is preferable to the baseline comparison models which indicates that in all cases some struct ure is preferable to no structure or a saturated model. The question then become s which model best fits each type of SDLP. For induced SDLPs the preferred model is equivocal as both sets of fit statistics are nearly identical. Due to the fact that they are nested, an examination of the change in RMSEA suggests that the interaction model is slightly preferable. In the case of synergistic projects the higher NFI, CFI and significantly lower ECVI suggests that the non interaction model is a better fit for this data. For voluntary projects, again the higher NFI, CFI and significantly lower ECVI suggest that the non interaction best fits the data. For scanning SDLPs, the reduction in RMSEA combined with the closeness of the other fit indices (except ECVI) suggest s that the interaction model is a better fit. An examination of the structural path coefficients of both the non interaction and interaction models support the selections made above. In the case of induced SDLPs, the main effects of AFT and PCAFT on proj ect use are not significant in either model while the interaction term is which only supports H ypothesis 2. For synergistic projects, the

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85 main effects of AFT and PCAFT on project use were significant in both models, but the interaction term was not signifi cant, which supports hypotheses 1 and 3. In the case of voluntary SDLPs, only the main effect of AFT on project use was significant and it was significant in both models, which supports hypothsis1 only. For scanning projects, the main effect of AFT was sig nificant in both models while PCAFT was not while the interaction term was significant as well, which supports hypotheses 1 and 2. These findin gs are further investigated below. Table 10. Structural Equation Model Results Model Fit: Induced SDLP's Fit S atistic Non Interaction Interaction Saturated Independence RMSEA 0.079 0.076 N/A 0.281 90% CI Low 0.068 0.065 N/A 0.273 90% CI High 0.091 0.087 N/A 0.290 RMR 0.107 0.109 0.000 0.595 GFI 0.853 0.852 1.000 0.245 AGFI 0.815 0.816 N/A 0.165 PGFI 0.678 0 .685 N/A 0.221 NFI 0.893 0.885 1.000 0.000 PNFI 0.788 0.788 0.000 0.000 RFI 0.878 0.871 N/A 0.000 IFI 0.938 0.935 1.000 0.000 TLI 0.929 0.927 N/A 0.000 CFI 0.937 0.935 1.000 0.000 PCFI 0.828 0.832 0.000 0.000 ECVI 2.095 2.246 2.121 16.191

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86 Mode l Fit: Synergistic SDLP's Fit Satistic Non Interaction Interaction Saturated Independence RMSEA 0.079 0.077 N/A 0.282 90% CI Low 0.068 0.066 N/A 0.273 90% CI High 0.091 0.088 N/A 0.290 RMR 0.133 0.132 0.000 0.625 GFI 0.851 0.850 1.000 0.244 AGFI 0.8 12 0.813 N/A 0.164 PGFI 0.676 0.684 N/A 0.220 NFI 0.892 0.884 1.000 0.000 PNFI 0.788 0.787 0.000 0.000 RFI 0.878 0.870 N/A 0.000 IFI 0.937 0.934 1.000 0.000 TLI 0.928 0.926 N/A 0.000 CFI 0.937 0.934 1.000 0.000 PCFI 0.827 0.831 0.000 0.000 ECVI 2. 111 2.265 2.121 16.224 Model Fit: Voluntary SDLP's Fit Satistic Non Interaction Interaction Saturated Independence RMSEA 0.079 0.076 N/A 0.281 90% CI Low 0.068 0.066 N/A 0.273 90% CI High 0.090 0.087 N/A 0.290 RMR 0.111 0.111 0.000 0.602 GFI 0.852 0.851 1.000 0.244 AGFI 0.814 0.815 N/A 0.165 PGFI 0.677 0.685 N/A 0.221 NFI 0.892 0.885 1.000 0.000 PNFI 0.788 0.787 0.000 0.000 RFI 0.878 0.871 N/A 0.000 IFI 0.937 0.935 1.000 0.000 TLI 0.929 0.926 N/A 0.000 CFI 0.937 0.934 1.000 0.000 PCFI 0.82 7 0.831 0.000 0.000 ECVI 2.101 2.256 2.121 16.192

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87 Model Fit: Scanning SDLP's Fit Satistic Non Interaction Interaction Saturated Independence RMSEA 0.078 0.075 N/A 0.281 90% CI Low 0.067 0.065 N/A 0.273 90% CI High 0.089 0.086 N/A 0.290 RMR 0.116 0.117 0.000 0.635 GFI 0.854 0.853 1.000 0.243 AGFI 0.816 0.817 N/A 0.163 PGFI 0.679 0.686 N/A 0.220 NFI 0.894 0.887 1.000 0.000 PNFI 0.789 0.789 0.000 0.000 RFI 0.880 0.873 N/A 0.000 IFI 0.939 0.937 1.000 0.000 TLI 0.930 0.928 N/A 0.000 CFI 0.939 0.936 1.000 0.000 PCFI 0.829 0.833 0.000 0.000 ECVI 2.078 2.228 2.121 16.210 Non Interaction Model Structural Paths SDLP: Induced Synergistic Voluntary Scanning Path Weight Sig. Weight Sig. Weight Sig. Weight Sig. PCAFT > SDLP Use 0.47 0.241 1.34 0.015 0.09 0.825 0.63 0.211 AFT > SDLP Use 0.38 0.173 0.82 0.033 0.66 0.016 1.16 0.001 Interaction Model Structural Paths SDLP: Induced Synergistic Voluntary Scanning Path Weight Sig. Weight Sig. Weight Sig. Weight Sig. PCAFT > SDLP Use 0.5 3 0.184 1.35 0.015 0.09 0.810 0.5 5 0.253 AFT > SDLP Use 0.45 0.105 0.83 0.032 0.67 0.015 1.23 0.000 PCAFTxAFT > SDLP Use 0.9 1 0.032 0.09 0.884 0.12 0.774 0.92 0.085 Nested r egression m odels To further clarify and verify the r esults of the S EM models, the mean centered predictor variables were entered into a stepwise regression with each of the types of SDLPs as the dependent variable. This was done to allow for a clearer picture of which of the hypotheses were supported and how the addition of paths impacted both model fit and the explanatory power of the model. In the stepwise entry, first the strength of the AFT to SDLP relationship was tested, then the PCAFT to SDLP

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88 path was added, and finally, the interaction term was added. Each step pro duced a nested F value (how the path impacts model fit) and a change in adjusted r square (how the path impacts the e xplanatory power of the model). The results of the nested models for each of the types of SDLPs can be seen in Table 11 These results len d credence to the findings of the SEM models when it comes to the hypotheses they support. In the case of Induced SDLPs, the main effects of both interaction of the two variabl es does significantly influence the use of this type of project. Additionally, the inclusion of the interaction term creates a significant increase in model fit (F = 4.327, p = .038) as well as a sizable jump of 1.4% in the explanatory power if the model. use of this type of project, but there is no interactive effect. The inclusion of the AFT and PCAFT main effect paths significantly contribute to both model fit (F = 2.919 and F = 5.999 respectively) and in the explanatory power of the model (see Table 11 ), while the inclusion of the interaction term does not help model fit and actually hurts the explanatory power of the model. The results for voluntary SDLPs show that only the ma in effect of AFT on the use of this type of SDLP is significant (F = 5.731) while the inclusion of the other two paths do not improve model fit and hamper the explanatory power of the model. Finally, the results for scanning SDLPs show that both the main effect of AFT does influence the use of this type of SDLP while PCAFT does not directly impact its use (F = 10.543 and 1.338 respectively) and the main effect does significantly increase model fit and explanatory

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89 power. Additionally, the interaction of AF T and PCAFT has an impact on the use of explanatory power by 0.8%. A discussion of the meanings and implications of these results is discussed below.

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90 Table 11. Nested Re gression Results SDLP: Induced Model 1 Model 2 Model 3 Path Beta Sig Beta Sig Beta Sig AFT > SDLP Use 0.086 0.227 0.089 0.212 0.106 0.137 PCAFT > SDLP Use N/A N/A 0.084 0.24 0.094 0.185 PCAFTxAFT > SDLP Use N/A N/A N/A N/A 0.148 0.038 Model F val ue 1.468 0.227 1.429 0.242 2.426 0.067 Nested F value N/A N/A 1.378 0.24 4.372 0.038 Adjusted R square 0.002 0.004 0.021 SDLP: Synergistic Model 1 Model 2 Model 3 Path Beta Sig Beta Sig Beta Sig AFT > SDLP Use 0.121 0.089 0.127 0.072 0.127 0.072 PCAFT > SDLP Use N/A N/A 0.171 0.015 0.172 0.015 PCAFTxAFT > SDLP Use N/A N/A N/A N/A 0.007 0.924 Model F value 2.919 0.089 4.496 0.012 2.985 0.032 Nested F value N/A N/A 5.999 0.015 0.009 0.924 Adjusted R square 0.01 0.034 0.029 SDLP: Voluntary Model 1 Model 2 Model 3 Path Beta Sig Beta Sig Beta Sig AFT > SDLP Use 0.168 0.018 0.168 0.018 0.17 0.018 PCAFT > SDLP Use N/A N/A 0.003 0.967 0.004 0.953 PCAFTxAFT > SDLP Use N/A N/A N/A N/A 0.018 0.796 Model F value 5.731 0.018 2.852 0.06 1.915 0.128 Nested F value N/A N/A 0.002 0.967 0.067 0.796 Adjusted R square 0.023 0.018 0.014 SDLP: Scanning Model 1 Model 2 Model 3 Path Beta Sig Beta Sig Beta Sig AFT > SDLP Use 0.225 0.001 0.223 0.002 0.236 0.001 PCAFT > SDLP Use N/A N/A 0.08 0. 249 0.072 0.298 PCAFTxAFT > SDLP Use N/A N/A N/A N/A 0.117 0.093 Model F value 10.543 0.001 5.949 0.003 4.95 0.002 Nested F value N/A N/A 1.338 0.249 2.841 0.093 Adjusted R square 0.046 0.048 0.056

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91 Discussion The results of the SEM and Nested regr ession models present some interesting findings. The findings for induced self directed learning projects indicate that while an do not directly impact the frequency of his/her use of this type of SDLP, the interaction between the two does influence the use of these SDLPs. This finding is supportive of H ypothesis 2 but does not support H ypotheses 1 and 3. The results are not surprising in light of conversations with t he employees who noted that most of the induced projects they engage in can be taken online. The use of this type of delivery format would be influenced by the combination of AFT and PCAFT, but not by either one separately because it takes the both an indi vidual who has an affinity for technology to be willing to use it, but also that individual has to feel that the firm also has an affinity for technology before they will put forth the effort to use it. When it comes to synergistic SDLPs, H ypotheses 1 and 3 were supported. This can be interpreted that both AFT and PCAFT do influence the extent to which an employee will use this type of SDLP, but that they operate independently of each other. This makes sense based on the nature of synergistic projects that are taken on by choice which explains the effect of affinity for technology because the employee has to be willing to utilize the information in the format in which it is provided (usually based on some type of technological platform). This type of project also rel ies on the availability of firm resources which explains the effect of perceived corporate affinity for technology on the frequency of its use, specifically based social exchange theory basis of this

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92 hypothesis that states that if the firm provide s a resource for employees, then they will reciprocate by utilizing that resource for the betterment of the firm. The results related to voluntary SDLPs and the fact that only H ypothesis 1 is supported are not surprising when one considers the nature of vo luntary projects. These projects are self created and monitored and rely heavily on the individual with minimal firm involvement. Therefore it makes sense that only affinity for technology influences the frequency of use of this type of project because the se projects also require a great deal of information literacy to implement which is augmented by the use of technology. Because the firm is not heavily involved in this type of project and may not even know it is occurring explains why employee perception s of corporate affinity for technology does not directly influence this type of project nor does it interact with personal affinity for technology. While scanning SDLPs are similar to voluntary in that they are self initiated, they differ in that they are ongoing, and this difference explains why the use of scanning projects are influenced differently. In this case, like voluntary projects, affinity for technology positively influences the use of scanning projects because of the information literacy require d to implement them and the assistance technology renders in this process. The moderating effect of PCAFT also makes sense for this type of project because of their continuous nature that a perception of an organizational climate that is supportive of the use of technology would encourage their use while the perception of an unsupportive climate would reduce the strength of the AFT to SDLP use link These findings support H ypotheses 1 and 2.

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93 The findings also support the typology of Clardy (2000) which, bas ed on the descriptions of each type of SDLP, suggest that each type of SDLP is unique and therefore each type should be affected by antecedents differently than the others. These findings also have important implications for academicians and managers that are discussed below. The supported hypotheses for each type of SDLP are summarized in Table 12. Table 12. Hypothesis Results Self Directed Learning Project Type Hypothesis Induced Synergistic Voluntary Scanning H 1 : Personal affinity for technology is positively related to employee use of self directed learning projects. Not Supported Supported Supported Supported H 2 : Perceived corporate affinity for technology will moderate the relationship between personal affinity for technology and employee use of self directed learning projects. Not Supported Supported Not Supported Not Supported H 3 : Perceived corporate affinity for technology is positively related to employee use of self directed learning projects. Supported Not Supported Not Supported Supported Contributions This paper contributes to the sales and services literatures by developing and testing a model that extends the current thinking on what drives employee use of self directed learning projects. The model shows that getting employees to eng age in voluntary and scanning self directed learning projects is both a selection issue and an internal marketing issue. On the selection side, this model shows the importance of hiring and retaining those customer contact employees who have a high affinit y for technology

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94 as they are more likely to engage in the use of self directed learning projects that benefit the firm (e.g., voluntary SDLPs) in addition to being more open to the increasingly important technological advances. On the internal marketing si de, this model shows the employee use of certain SDLPs (e.g., induce and scanning SDLPs). Also, this paper helps to establish the nomological network around the new constru ct of perceived corporate affinity for technology. The incorporation of this new construct into the literature also helps to demonstrate the importance of employee perceptions of corporate attitudes on behaviors that produce important outcomes for the firm the model shows how both individual and perceptions of firm level affinity for technology can improve employee profitability by improving their use of certain types of SDLPs that can result in better knowledge which translates into more sales and better services. However, it also shows that depending on the type of self directed learning projects desired (i.e., voluntary SDLPs), getting employees to do them may be contingent o n proper selection (AFT), while other projects (i.e., induced and scanning SDLPs) may be contingent on the type of environment fostered by the firm, and still others (synergistic) may depend on both. Finally, this paper shows how important personal affinit y for technology and perceptions of corporate affinity for technology are in creating a competitive advantage. The extended knowledge base that employees develop through the use of voluntary and scanning SDLPs is a competitive advantage that is difficult f or understanding of w hat is needed to be successful.

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95 Future Research From the model above, several avenues of future research are available. The first area of future research i s to identify barriers to both salesperson affinity for technology and their perception of corporate affinity for technology so that ways to overcome them can be developed. These barriers may be of either a personal or corporate nature, but in either case, according to the proposed model, they impede the ability of the salesperson to fully leverage the benefits of the technology available for either direct selling tasks or learning. Another area for future research is to test the propositions in this model in conjunction with the model proposed by Artis and Harris (2007) in order to determine the incremental contributions of each of the determinants of salesperson use of self directed learning projects. This is a necessary area of study as it provides acade mic and practitioners with an understanding of the key areas to address in order to better allocate their efforts.

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96 References Artis, A. B. & Harris E. G. (2007). Self Directed l earning and s ales f orce p erformance: An i ntegrated f ramework Journal o f Personal Selling and Sales Management 29 (1), 9 24. Breivik, P. S (2005). 21st c entury l earning and i nformation l iteracy Change: The Magazine of Higher Learning 37 (2), 21 27. Churchill, G. A. (1979) A p aradigm for d eveloping b etter m easures of m ark eting c onstructs Journal of Marketing Research 16 (1), 64 73. Clardy, A. (2000). Learning on t heir o wn: Vocationally o riented s elf d irected l earning p rojects Human Resource Development Quarterly 11(2), 105 125. Cron, W L., Marshall, G. W., Singh, J., Spiro R. L., & Sujan H. (2005) Salesperson s election, t raining, and d evelopment: Trends, i mplication, and r esearch o pportunities Journal of Personal Selling & Sales Management 25 (2), 123 136. Edison, S W. & Geissler G. L. (2003). Measuring a ttitud es t owards g eneral t echnology: Antecedents, h ypotheses and s cale d evelopment Journal of Targeting, Measurement and Analysis for Marketing 12 (2), 137 156. Ellinger, A. D. (2004) The c oncept of s elf d irected l earning and i ts i mplications for h uman r esour ce d evelopment Advances in Developing Human Resources 6 (2), 158 177. Fleming, D. & Artis A. B. ( forthcoming ). Measuring c orporate a ffinity for t echnology: A s cale for c ustomers and e mployees Journal of Personal Selling and Sales Management Special Is e lling: Threats and Geissler, G L. & Edison S. W. (2005) Market m a ttitudes t owards g eneral t echnology: Implications for m arketing c ommunications Journal of Marketing Communications 11 (2), 73 94. Goff B G., Boles, J. S., Bellenger D. N., & Stojack C. (1997) The i nfluence of s alesperson s elling b ehaviors on c ustomer s atisfaction with p roducts Journal of Retailing 73 (2), 171 183.

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97 Goldman, R D., Platt B. B., & Kaplan R. B. (1973). Dimensions of a ttitudes t oward t echnology Journal of Applied Psychology 57 (2), 184 187. Harris, E. G. & Fleming D. E. (2005) Assessing the h uman e lement in s ervice p ersonality f ormation: Personality c ongruency and the f ive f actor m odel Journal of Services Marketi ng 19 (4), 187 198. Heinssen, R K., Glass C. R., & Knight L. A. (1987). Assessing c omputer a nxiety: Development and v alidation of the c omputer a nxiety r ating s cale Computers in Human Behavior 3 (1), 49 59. Hurley, R F. (1998). Customer s ervice b ehav ior in r etail s ettings: A s tudy of the e ffect of s ervice p rovider p ersonality Journal of the Academy of Marketing Science 26 (2), 115 127. Hurley, R F. (2002) Putting p eople b ack i nto o rganizational l earning Journal of Business and Industrial Marketin g 17 (4), 270 281. Jarvis, C B MacKenzie S. B., & Podsakoff P. M. (2003). A c ritical r eview of c onstruct i ndicators and m easurement m odel m isspecification in m arketing and c onsumer r esearch Journal of Consumer Research 30 (2), 199 218. Jones, E. B rown, S. P., Zoltners A. A., & Weitz B. A. (2005) T he c hanging e nvironment of s elling and s ales m anagement Journal of Personal Selling & Sales Management 25(2), 105 111. Knowles, M S. (1975) Self Directed l earning: A g uide for l earners and t eacher New York NY : Association Press. Lindell, M K. & Whitney D. J. (2001) Accounting for c ommon m ethod v ariance in c ross s ectional r esearch d esigns Journal of Applied Psychology 86 (1), 114 121. Lumpkin, J R. & Darden W. R. (1982). Relating t elevision p reference v iewing to s hopping o rientations, l ife s tyles and d emographics: The e xamination of p erceptual and p reference d imensions of t elevision programming, Journal of Advertising 11( 4 ), 56 67. Lupton, R A., Weiss, J. E., & Peterson R. T. (1999). Sal es t raining e valuation m odel (STEM) : A c onceptual f ramework Industrial Marketing Management 28 (1), 73 86. MacKenzie, S B. (2003). The d angers of p oor c onstruct c onceptualization Journal of the Academy of Marketing Science 31 (3), 323 326.

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98 Mackey, T R. (2005). Web d evelopment in i nformation s cience u ndergraduate e ducation: Integrating i nformation l iteracy and i nformation t echnology Journal of Education for Library and Information Science 46 (1), 21 35. Marshall, G W., Moncrief W. C., & Lassk, F. G (1999) The c urrent s tate of s ales f orce a ctivities Industrial Marketing Management 28 (1), 87 98. Neal, A and Griffin M. A. (2006) A s tudy of the l agged r elationships a mong s afety c limate, s afety m otivation, s afety b ehavior, and a ccidents at the i n dividual and g roup l evels Journal of Applied Psychology 91 (4), 946 953. Parasuraman, A. (2000). Technology r eadiness i ndex (TRI): A m ultiple i tem s cale to m easure r eadiness to e mbrace n ew t echnologies Journal of Service Research 2 (4), 307 320. Parasu raman, A., Zeithaml V. A., & Berry L. L. (1988). SERVQUAL: A m ultiple item s cale for m easuring c onsumer p erceptions of s ervice q uality Journal of Retailing 64(1), 12 40. Ping, R A. (1996) Latent v ariable r egression: A t echnique for e stimating i nter action and q uadratic c oefficients Multivariate Behavioral Research 31 (1), 95 120. Podsakoff, P M. & Organ D. W. (1986). Self Reports in o rganizational r esearch: P roblems and p rospects Journal of Management, 12 (4), 531 544. Rossiter, J R. (2003) Th e C OAR SE procedure for scale development in marketing International Journal of Research in Marketing 19 (4), 305 335. Segars, A. H. (1997). Assessing the u nidimensionality of m easurement: A p aradigm and i llustration within the c ontext of i nformation s y stems r esearch Omega 25 (1), 107 121. Singh, J and Rhoads G. K. (1991). Boundary r ole a mbiguity in m arketing o riented p ositions: A m ultidimensional, m ultifaceted o perationalization Journal of Marketing Research 28 (3), 328 338. Sujan, H Weitz B. A ., & Kumar N. (1994) Learning o rientation, w orking s mart, and e ffective s elling Journal of Marketing 58 (3), 39 52. Thibaut, J J. & Kelley H. H. (1959) The s ocial p sychology of g roups New York, NY: John Wiley and Sons. Tough, A (1967) Learning w ithout a t eacher Toronto: Ontario Institute for Studies in Education.

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99 Turley, D & Geiger S. (2006) Exploring s alesperson l earning in the c lient r elationship n exus European Journal of Marketing 40(5/6), 662 681.

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100 Appendix 2 : Survey I tems Affinity for Technology (Edison and Geissler 2003) 1. I enjoy learning new computer programs and hearing about new technologies 2. If I am given an assignment that requires that I learn to use a new program or how to use a machine, I usually succeed. 3. Solving technological problems seems like a fun challenge 4. Technology is my friend 5. I find most technology easy to learn 6. Peop 7. I relate well to technology and machines 8. I am comfortable learning new technology 9. I know how to deal with technological malfunctions or problems 10. I feel as up to date o n technology as my peers. Perceived Corporate Affinity for Technology (Pretest) 1. My company views technology as a friend 2. My company offers the latest technologies . 4. My company seems comfortable implementing new technology 5. My company relates well to technology 6. My company knows how to d eal with technological problems 7. I feel my company is as up to date on technology as its competitors 8. My company shows its relationship with technology by offering secure technology based services Induced Self Directed Learning Projects In the past six months, how many training programs have you attended that were required either by your company or a regulatory agency that lasted at least seven hours (not necessarily consecutively)? For example mandatory training on new service offerings or governme nt mandated continuing education about banking regulations. Synergistic Self Directed Learning Projects In the past six months, how many times have you used company provided resources for a minimum of seven hours (not necessarily consecutively) to learn a bout a job related

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101 library to learn about a specific topic. Voluntary Self Directed Learning Projects In the past six months, how many times have you identified a job r elated topic you needed to know more about and spent at least seven hours (not necessarily consecutively) learning about that topic? For example, if you decided you needed to know more about or more hours, then that would be one instance. Scanning Self Directed Learning Project Sometimes it is necessary to engage in ongoing learning to become a better service provider. For instance you may feel that you have to constantly monitor competitors offers in order to be able to effectively assist clients, or you may determine that you need to monitor discussions at the federal level regarding you business to better inform customers of their best options. How many of these types of continuous learni ng projects have you engaged in over the past six months? Fashion Conscientiousness (Lumpkin and Darden 1982) Please rate your agreement with how well the following statements describe you ( = Strongly When I must cho ose between the two, I usually dress for fashion, not for comfort An important part of my life and activities is dressing smartly A person should try to dress in style Demographics What is your age? Under 20 20 29 30 39 40 49 50 59 60 or older What is your gender? ______________ How long have you worked at Bank X ? Less than 1 year 1 3 Years 4 6 years 7 9 years 10 12 years 13 or more years What is your current position at Bank X ? Branch Manager Assistant Manager Teller Financial Service Rep Investment Advisor How long have y ou worked in the banking industry? Less than 1 year 1 5 years 6 10 years 11 15 years 16 20 years More than 20 years Branch ________________

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102 Developing the Nomological Network of Perc eived Corporate Affinity For Technology: Study 3 The E ffect of C ustomer P erceptions on S ervice O utcomes The challenge facing service firms in as they shift towards a higher reliance on technology has moved from getting customers to utilize the technolo gy to getting customers to see the firm as able to provide these technology based offerings. Customer perceptions of the firm serve as the basis for several significant streams of literature in the marketing domain. For instance, it has been well establish ed that consumers select firms that they perceive as having personality traits in common with themselves (Sirgy and Samli 1985). However, no one has examined whether customer perceptions of firm attitudes toward particular objects or ideas produce similar effects, or if these perceptions are relevant to key outcomes for both customers and firms. This paper examines these issues from the perspectives of signaling theory, congruity theory and service performance literature to develop and test a model of the relationships between customer perceptions of corporate affinity for technology on perceptions of service perfor mance and key service outcomes. Introduction The use of technology in the service sector continues to rise because of the benefits it offers technologies, especially self service ones, are an opportunity to reduce overhead

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103 expenses and standardize service experiences. From a customer perspective, these same technologies provide the benefits of more convenience and a more consistent service delivery. Honebein and Cammarano (2006) note that these technologies allow customers cr Traditionally, the obstacle to this mutually beneficial relatio nship has been viewed as an issue of customer willingness to use the available technologies (Parasuraman 2000), but a recent article by Woodall, Colby and Parasuraman (2007) notes that service customers are technologically savvy and predict that the serv ices domain will experience a significant shift as customers demand more technology based facing firms is finding ways to show customers that the firm is capable of effe ctively, efficiently and securely delivering on this new generation of services. There have been several studies on the inherent properties of the technology itself that make it more likely to be adopted or used by individuals (Curran and Meuter 2005) and studies on what individual level factors influence adoption decisions (Parasuraman 2000), but no studies have been published as yet examined the role that the attitude the company projects about its feeling toward technology influences customer percep tio ns of the service outcomes. The purpose of this paper is to investigate the how customer perceptions of firm attitudes can influence their evaluation of the service experience and ratings of service outcomes including how well the service met their expecta tions, their overall satisfaction with the firm and whether or not they are willing to recommend the firm to others. Specifically, this paper will examine the impact of customer perceptions of firm affinity for technology on theses key service outcomes as well how this relationship is moderated

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104 by customer personal affinity for technology. This paper begins with a review of the pertinent literature, which is then used to develop a model of customer perceptions of firm affinity for technology and how these p erceptions influence key customer outcomes of service encounters. Literature R eview Personification of firms The consumer behavior literature frequently contains research based on the notion that customers project human characteristics onto innate objec ts, and many of these articles utilize measures of human traits to evaluate firms or brands. Granted, it is impossible for an inanimate object such as a company to actually possess traits or attitudes; however, evidence suggests that people do tend to assi gn human traits to firms through a process of anthropomorphism (Brown 1991). Brown (1991) also states that giving human characteristics to inanimate objects seems to be a universal occurrence and that the personification of firms allows people to better e xpress their evaluative judgments. McGill (2000) notes that people place brands in to natural categories just like they do other people and animals. According Levesque (2003 and other commercial objects in terms of human attributes is likely to be useful for the elaboration and implem One of the most common examples of applying the anthropomorphism of firms to marketing research is the Brand Pers onality scale developed by Aaker (1997) to assess of 347). This scale has prompted much research on the influence brand personality exerts on

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105 and Levesque (2003) extended the brand personality concept by developing a measure of personality for stores. To date, most studies of this type only assume that customers project human traits onto firms, but none examine whether attitudes towards other o bjects or ideas are also attributed to companies. This is important as customers develop commitments to causes (e.g., a healthy environment) and objects (i.e., technology) and these customer attitudes influence company communication efforts and actions. Fo r instance, the increase in consumer concerns about the environment influenced Wal the image that the company is concern about the issue as well (Gunther 2006). Individual p erceptions of t echno logy In the study of the impact of technology on customers there has been a wide array of measures developed and studies conducted to determine how individuals interact with technology. Goldman, Platt and Kaplan (1973) conducted some of the earliest wor k in this area with their research on the dimensions of attitudes toward technology. Their work with students of various academic majors indicates that most differences between groups, were due to differences in mechanical curiosity (mechanical competence and a preference for technical rather than humanistic events), but not the other factors such as alienation (societal unconcern with the individual), spiritual benefits (consider technology as rapid and dramatic way of solving problems) or global mechanism (a positive or negative global attitude toward technology). The key finding here was that the global dimension of attitude toward mechanization did not differentiate between student groups as would have been expected a priori, but this finding is not sur prising given their use of a student population which would tend to be more homogeneous than randomly selected samples from the general

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106 population. Thus, the notion that groups can be differentiated based on global attitude toward technology should not be dismissed as the samples used in this study were likely to be very similar in key factors such as age, life style and education level. If this same study were conducted on the client base of a firm the results may be drastically different due to the hetero ge neous nature of the population. The work of Heinssen, Glass and Knight (1987) on computer anxiety provides another examination of individual level perceptions of technology. According to their findings, higher levels of computer anxiety are related to lower levels of computer experience and lower mechanical interest. This indicates that people who are uncomfortable with computers are less likely to use computers and are less interested in l earning about new technologies. Parasuraman (2000) developed the Technology Readiness Index (TRI), a scale designed to measure the willingness of an individual to adopt new technologies. His four facet scale uses a combination of optimism, innovativeness, discomfort and insecurity as personality traits that predict the readiness of an individual to adopt new technologies. Curran, Meuter and Suprenant (2003) determined that customer intentions to use self service technologies were influenced by attitudes towards both the interpersonal and technology aspects of the serv ice experience. In their 2005 work, Curran and Meuter found that the ease of use, usefulness and risk associated with certain technologies were significant predictors of attitude toward self service technologies, and attitude then predicted intentions to u se the technologies. Curran and Meuter (2007) found that customers were more influenced by fun than by utility in deciding to a dopt self service technologies.

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107 Edison and Geissler (2003) developed a construct of affinity for technology, which they define as positive affect toward technology (in general) on the afore mentioned work of Goldman and Kaplan (1973), Heinssen, Glass and Knight (1987) and Parasuraman (2000) as well as studies by Simpson and Troost (1982) on the antecedents of learning science and Brosnan (1998) on computer anxiety and personality traits. Their study is concerned with the attitude people hold toward technology. They found several antecedents of affinity for technology including optimism, need for cognition, self efficacy, age, and gender. In the only other published study to utilize this scale, Geissler and Edison (2005) found that affinity for technology was positively related to market mavenism. Additionally, this study repeated both the exploratory factor anal ytic and confirmatory factor analytic techniques used in their first study with similar results, which indicates that the factor structure of this construct holds up over different the .89 they found in the original scale development study. While the previously identified scales may possess many similarities, the Edison and Geissler (2003) scale differs from the other scales in two distinct ways. First their scale is concerned with the affect people feel toward technology while the Parasuraman distinction may seem small it is important as many times consumers do not necessarily adopt of their own voliti on but rather are forced to adopt by changes in corporate business models that mandate the use of self service technologies. For instance, in the mid adopting automatic teller machines (ATMs) or paying a $3 per transaction fee to conduct

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108 business via a teller. The second important distinction is that their scale explicitly looks at general technology while the other scales measuring affect/attitude towards technology hav e been focused on technology that consists primarily of computers and the internet (exception being Goldman, Platt and Kaplan 1973). This focus on computers and the Internet as technology reflects a wide spread misconception of what constitutes technolog Literacy (Pearson and Young 2002 ). T hese differences from the other scales related to (2003) scale a more useful measu re for the purpose of this paper. A proposed extention of the affinity for technology construct is that of customer and employee perceptions of corporate affinity for technology (Fleming and Artis forthcoming ) the perception individuals have of the affect (p. 8) This construct places emphasis on is very different from how the individual feels about technology which is what is inity for technology scale This construct was developed and validated in pretests utilizing both qualitative and quantitative methods They note, based on qualitative findings, that these perceptions of corporate affinity for technology can be derived from many different points of contact with an organization such as advertisements, encounters with employees and contact with managers. The ir quantitative exploratory and confirma tory factor analys e s found that the same eight item solution created the best factor structure for both customers and employees, and a correlational analys e s found that from a customer perspective this

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109 construct is related to both personal affinity for tec hnology (r=.34) and perceptions of service performance (r=.69) Self Congruity The notion of self congruity was introduced to the marketing literature by Sirgy (1980 1981, 1982a, 1982b) in a series of studies. His work is based on the psychological view that people possess multiple self concepts. The key self concepts he focuses on are the ideal self, the actual self and the social self. In his work (Sirgy 1982c), the ideal self is defined as how an individual would like to see himself or herself. The a ctual self is defined as how an individual views himself or herself. The social self is defined as how an individual would like others to see him or her. His work revealed that consumers were more likely to select products that possessed traits which were consistent with pos itive aspects their self image. (1982c) work also showed that this congruity has a strong influence on purchase motivation. Sirgy and Danes (1982) formalized the mathematical models that reflect the influence of self image and pr oduct image. The work of Sirgy, Johar, Samli and Claiborne (1991) determined that customer evaluations of products are biased by the extent to which self related attributes are processed because these perceptions of self product congruity influence how in coming information about the environment is Sirgy and Samli (1985) found that customers are more likely to purchase products from stor es whose image is consistent with their own self image. Another example of this extension of the self congruity research is the work of Lau and Phau (2007), which examined the importance of brand personality for

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110 symbolic brands versus functional brands, an d found that brand personality congruity is an important influence on consumer choice in both cases. Studies by Harris and Fleming (2005) and Ekinci and Riley (2003) have found that that personality congruency is important in service settings. The Ekini a (2003) study indicate that personality congruence is correlated with service outcomes like customer satisfaction. Additionally, Harris and Fleming (2005) found that the relationship between personality congruence and service outcomes is mediated by perceived service performance. This is in line with the findings of Sirgy, Johar, Samli and Claiborne (1991) that self congruity influences perceptions of service outcomes and the research of Parasuraman, Zeithaml and Berry (1994) that states that c ustomer perceptions of service performance are created during service episodes and occur before service outcomes. Service e xperience In the service literature there have been many scales proposed to measure customer service experiences. Perhaps the two best known are the SERVQUAL scale (Parasuraman, Zeithaml and Berry 1985, 1988) and the SERVPERF scale (Cronin and Taylor 1992). The two scales seem very similar on a superficial level because they both contain the same five dimensions of service quality (reliability, assurance, empathy, tangibles, responsiveness). However, they are vastly different in terms of what they actually measure and how they measure it. The SERVQUAL scale (Parasuraman, Zeithaml and Berry 1985, 1988) is based on the notion of di sconfirmation of expectations and measures both customer expectations and their perceptions of the experience. This means that the score for a SERVQUAL measure is a difference score between what the customer expected and what he or she actually experienced thus a high negative score indicates that the experience was much worse

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111 than expected and a high positive score indicates an experience that wa s much better than anticipated. There have been some criticisms of this scale because of the method it employs Peter, Churchill and Brown (1993) note that the use of difference scores in consumer research can lead to problems such as reliability problems depending on the reliabilities of the components and their correlation with one another, discriminant validi ty problems due to low reliability creating an illusion of discriminant validity, spurious correlation problems because of the relationship between the difference scores and their components, and variance restrictions problems that occur when one component score is consistently higher than the other. To overcome the issues raised about a difference score measure of service experience, Cronin and Taylor (1992) developed the SERVPERF scale. It uses the same facets as the SERVQUAL scale, but being a difference score measure, it is a direct measure of customer perceptions without the expectations component. The arguments against this measure include that without a measure of expectations, the scale is not as diagnostic as it does not indicate where significant g aps exist in service delivery like the SERVQUAL does (Parasuraman Zeithaml and Berry 1994). A detailed account of the debate over which scale is more valid is beyond the scope of this article, however, for a more comprehensive discussion of the argument s for each scale see Carrillat, Jaramillo and Mulki (2007) or Burch, Rogers and Underwood (1995). Because this paper is interested in how customer perceptions of firm attitudes influence service delivery perceptions and because its psychometric propertie s are a better fit to the proposed analytical methods, the Cronin and Taylor (1992) method is used in this current study.

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112 Service o utcomes In the services literature studies have examined a myriad of outcomes that result from customer perceptions of the ir service experience. Among the most common are disconfirmation of expectations, global satisfaction, and word of mouth intentions. Disconfirmation is defined as the discrepancy between what the customer expected of the service encounter and what was actu ally experienced (Day 1984). The literature has utilized two different ways to measure disconfirmation: subtractive and subjective. Subtractive disconfirmation is measured similarly SERVQUAL in that it measures both expectations and actual experience and takes a difference scores to determine disconfirmation (Tse and Wilton 1988). This method is also subject to similar criticisms as SERVQUAL including those of Peters, Churchill and Brown (1993). Subjective disconfirmation is a more direct measure where c ustomer are asked to rate how well the experience matched up with what expected the service experience to be like (Oliver 1980). According to Oliver (1980) subjective disconfirmation includes cognitive processing of the experience and should produce a m ore robust predictor of satisfaction tha n subtractive disconfirmation. Satisfaction is defined by Oliver (1997) as a reaction to the favorable gratification of wants or needs. Brown, Berry, Dacin and Gunst (2005) note that satisfaction can be with a prod uct, a service, or a retailer and is an important response that occurs after purchase. This construct has been linked to a myriad of consequences such as customer retention (Rust and Zahorik 1993) and loyalty (Heskett, Jones, Loveman, Sasser and Schlesin ger 1994). Satisfaction has been measured in a variety of ways. For instance, Churchill and Suprenant (1982) measured attribute specific satisfaction in terms of both customers beliefs about their satisfaction with a particular attribute of the product as well

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113 as their affective reaction to their satisfaction with a particular attribute, and they measured satisfaction with a global item that reflects an overall evaluation of a product. This global measure has been previously used in services r esearch because of the variable nature of service experience (Harris and Fleming 2005). Word of mouth intention is another of the consequences of customer satisfaction that has been studied (Heckman and Guskey 1998, Swan and Oliver, 1989). Brown Barry, Dacin and Gunst (2005) defined word of mouth intentions as the spread of information about a product, service, or firm, from consumer to consumer. This outcome, influence cu stomer decisions (Sheth 1971) possibly because the source is seen as more believable than a persuasive message from the firm (Murray 1991). Model Development The relationship between customer perceptions of firm affinity for technology and customer per ceptions of service quality is based on the conceptual work of Honebein and Cammarano (2006). According to their article, technology is more than a way for firms to crea convenience/control provided to the customer should result in increased perc eptions of service performance. Additionally, customer perceptions of firm affinity for technology should infl uence service performance perceptions through it role as a signal to customers. Signaling theory is based in the economic study of asymmetric information conditions

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114 between buyers and sellers (Spence 1974). It is based on the notion that sellers know thei r true product quality prior to the sale, buyers do not; especially if these products contain experience properties (such as services), which can only be evaluated during consumption (Nelson 1970). One way firms can over come this information gap is to se nd signals about their quality. A variety of signals have been tested such as price (Milgrom and Roberts 1986), advertising (Ippolito 1990), and warranties (Boulding and Kirmani 1993). Boulding and Kirmani (1993) also found that the type of signals int erpreted by potential consumers influenced perceived quality ratings even without actually experiencing the product. From signaling theory it is possible to see the efforts of managers of firms to project an attitude of the firm toward technology as a way of signaling to customers that the firm is committed to providing the best service possible. According to Woodall, Colby and Parasuraman (2007) the model of service delivery is being altered as technology allows for more mobility, portability, personaliza tion and collaboration in services along with shifts in demographics and lifestyles, and they note that firms must Customer perceptions of corporate affinity for technol ogy should serve as a signal that the firm can provide this new generation of services. Therefore, customer perceptions of firm affinity for technology should influence customer ratings of service performance due to the experiential nature of services. Fro m this, following hypothesis is proposed: H1: Customer Perceived Corporate Affinity for Technology is positively related to customer rating of service performance.

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115 However, there is a limiting factor to the positive benefit of customer perceptions of corp orate affinity for technology on customer perceptions of service quality; namely, the customers own affinity for technology. According to the self congruity literature cited above, consumers are more likely to be loyal to a firm that they perceive as havin g an image (Sirgy and Samli 1985) consistent their own and are more likely to report a positive service experience if they perceive the firm as having personality traits congruent with their own (Harris and Fleming 2005). It is also logical to assume tha t customers are more likely to more positively view service experiences with firms that they perceive as holding a compatible attitude toward a particular idea or object. The fact that Wal how customers that they care about the environment (Gunther 2006) indicates that major companies also believe that having attitudes that are congruous with their customers is important to retaining serving bias pri Johar, Samli, and Claiborne (1991) states that customer evaluations of products are influenced by the extent to which customers perceive similarities between the attributes possessed by the product and themselves and these simil arities influences how incoming environmental information is processed by the customer. From this, the following hypothesis is proposed: H2: Customer personal affinity for technology moderates the relationship between Customer Perceived Corporate Affinity for Technology and customer ratings of service performance

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116 The relationship between perceptions of service performance and subjective disconfirmation is an intuitive one based on the idea that the better a consumer perceives the service performance the higher they will rate the service relative to their expectations. This link has been shown in the literature as well (i.e., Churchill and Surprenant 1982; Swan and Trawick 1980). Oliver and Bearden (1985) found in their work on disconfirmation and satisfac tion that the impact of inferred disconfirmation (the difference between expectations and performance) on satisfaction was partially mediated by the overall (subjective) measure of disconfirmation, indicating that performance does impact subjective disconf irmation. Additionally, Harris and Fleming (2005) found that service performance perceptions mediate the impact of customer service personality congruence on service outcomes including subjective disconfirmation. Schneider and Bowen (1999) also highlight t he importance of performance in customer subjective disconfirmation in their description of the Met Expectations model of customer satisfaction by noting that the actual service delivery will determine the extent to which r were not met. Based on this research and the way the subjective disconfirmation measure is scored (much worse than expected to much better t han expected), following hypothesis is proposed: H3: Service performance is positively related to subjective disc onfirmation. The link between subjective disconfirmation and overall satisfaction is well established in the literature (Oliver 1980; Olson and Dover 1976). Oliver and Swan

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117 (1989) also show a link between disconfirmation and satisfaction with the facet s of a retail buying experience that also impacts overall satisfaction. According to the Met Expectations model described by Schneider and Bowen (1999) customers enter a service encounter with some form of experienced based expectation, and their satisfac tion with the service experience is determined by the extent to which these expectations are met. For instance, if the experience is close to what the expected they will fall between moderately satisfied to moderately dissatisfied; but if the experience i s much better or worse than expected they will be extremely satisfied of extremely dissatisfied. By combining these findings and given how the measure of subjective disconfirmation is scored (much worse than expected to much better than expected), the foll owing hypothesis is derived: H4: Subjective disconfirmation is positively related to global satisfaction with the firm. The link between satisfaction and word of mouth intentions is another that has been well established in the literature. For instance, Westbrook (1987) found a direct link between both satisfaction and affect on word of mouth intentions. His findings corroborate the causal chain proposed by Oliver and Bearden (1985) that states that disconfirmation leads to satisfaction, satisfaction lead s to attitudes, and attitudes lead to intentions. Schneider and Bowen (1999) also note that extremely satisfied customers,

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118 t moderately satisfied. Based on these findings the following hypothesis is proposed: H5: Global satisfaction with the firm is positively related to customer word of mouth intentions. These hypotheses can be seen Figure 5 Figure 5. Conceptual Model Methods Sample In order to test this model, a sample of 3 49 customers was collected; this number provide s ample degrees of freedom based on the number of free parameters to be estimated. The sampling frame for this study was th e customer base of two region s of a large bank that operates in the Southeastern and Eastern United States. The bank has over $170 billion in assets and was selected as the financial services industry represents a prototypical service industry that relies on the selling skills of its customer contact employees and has been used in many other studies related to services (e.g. the SERVQUAL scale by Parasuraman, Zeithaml and Berry, 1988) and sales ( George, Kelly and Marshall 1986, Ridnour, Lassk and Shephe rd 2001 ) Th ese region s contain 22 Customer Perceived Corporate AFT SERVPERF Perceptions Subjective Disconfirmation Global Satisfaction with firm Word of mouth Intentions + H1 + H3 + H5 + H4 Customer AFT H2

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119 branches that are both urban and rural in nature providing a cross section of the overall customer one of Regional Vice President s The participants w ere selected using an every kth member selection technique over a three week period in the summer of 2009 The researcher spent at least 4 hours in each of the target branch es and ask ed every 3 rd person to fill out the survey until the time for that day was completed The response ra te ranged between 42% and 78% at the branch level and was 61.5% overall. Such a high response rate minimizes concerns over non response bias. However, to be sure, an additional comparison of the demographic characteristics of the sample to the profile of the area where the sample was collected was done to ensure that the sample was representative. A demographic profile of the respondents can be seen in Table 1 3 It shows that the sample to be a representative cross section of bank customers that utilize a variety of the available services and transaction media as well as a being a sampling that reflects the age profile of the branches service area. Table 13. Demographics Gender (n = 302) Bank Tenure in Months (n = 206) Male 52.00% Mean 86.38 Female 48.00% Median 60 Mode 12 Age (n = 299) Under 20 2.01% Transaction Frequency (n = 218) 20 29 15.05% 3+ Times/week 31.65% 30 39 19.06% 1 2 Times/Week 46.79% 40 49 23.75% 1 2 Times/Month 16.06% 50 59 20.40% 6 10 Times/ Year 2.29% 60 69 12. 71% 3 5 Times/Year 0.92% 70 79 6.35% < 2 Times/Year 2.29% 80 and older 0.67%

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120 Channel Use (n = 208) Service Type (n = 219) Lobby 95.20% Personal 64.38% Drive through 51.90% Commercial 8.20% ATM 47.10% Investments 0.46% Telephone 15.30% C ombination 26.94% Online 42.20% Measures Affinity for Technology was measured via a 10 item 6 point type scale developed by Edison fect and attitudes towards general technology. This scale has been psychometrically validated and shown to have s alphas ranging from .82 .89) (Edison and Geissler 2003; Geis sler and Edison 2005). In this Customer perceptions of corporate affinity for technology were measured on an 8 item 6 = strongly agree) developed and ha for the PCAFT scale was .97. Service Performance was measured via an adapted a 15 item measure of SERVPERF as used by Harris and Fleming (2005) based on the work of Cronin and Taylor (1992) and Brady, Cronin and Brand (2002). Each dimension of SERVPERF (reliability, assurance, tangibles, empathy, and responsiveness) conceptualized by Parasuraman Z eithaml, and Berry (1985, 1988) was measured by three items using a 6 point Likert type scale. Three item measures are utilized to shorten the survey and reduce respondent fatigue Previous work by Harris and Fleming (2005) indicates that this scale

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121 demons trates good psychometric properties (e.g. study the Cronbach The key service outcomes were measured by single items adopted or adapted from Harris and Fleming (2005) The global satisfac tion measure read, point Likert type scale that was anchored by (1) Extremely Dissatisfied and (6) Extremely Satisfied. The Compared to your overall expectations, how point Likert type scale anchored by (1) Much worse than expected and (6) Much better than expected. The word of kely is it that you would recommend BANK X point Likert type scale anchored by ( 1) Definitely would not and (6) Definitely would. There has been considerable debate in the literature about the use of single item indic ators, but the findings of Wanous, Reichers and Hudy (1997) indicates that they are useful if the content of the question is unambiguous as would be expected with these types of questions. Additionally, a 3 item measure of fashion consciousness (Cronbach alpha = .84) based on the work of Lumpkin and Darden (1982) was included in the survey as a test for common method bias. All survey items can be seen in Appendix 3 Analysis The first step in the analysis process was to subject the responses to all multi item scales to a confirmatory factor analysis (CFA). The purpose of the CFA is to determine if the same factor structure holds up for this different population (i.e. financial service industry customers) in the case of the personal affinity for technolog y scale and to provide a test of measurement problems for the rest of the scales. Because the measures

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122 are all single source, self reports, it was also necessary to test for common method bias. factor test, which indicates common method variance if a single factor is found in an unrotated solution or if a first factor explains a majority of the variance in all the measured variables (Podsakoff and Organ 1986). The second method is to take a mea sure of a completely unrelated construct (e.g. fashion consciousness) and examine its correlation with the constructs of interest. A significant correlation with this marker variable reveals the presence of common methods bias according to Lindell and Whi tney (2001). The second step in the analysis process was to conduct a series of path analyses to test the hypotheses. The first model tested was a full structural equation model in which both the measurement model and the structural paths are estimated si multaneously. It is important to note that all predictor variables were mean centered in order to reduce multicolinearity when the interaction is included in the model (Ping 1996). This test was conducted in two parts. First, a test of the direct effect o f both perceived corporate affinity for technology and personal affinity for technology on customer perceptions of service performance and the key outcomes was conducted. Second, an interaction model was used to test of the moderating effect of personal af finity for technology on the relationship between perceived corporate affinity for technology and customer perceptions of service performance. The interaction term was created by multiplying a ing it in the model as a single indicator latent construct with a fixed factor loading and error variance based on the loadings and variances of the non interaction model. This is the method proscribed by

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123 Ping (1996) for dealing with continuous variable in teractions in structural equation modeling. score for each scale is used in the correlation matrix as a single observed variable rather than estimating the measurement model and the structural paths simultaneously. This method was used to overcome the measurement issue that arose in the AFT scale as discussed in the confirmatory factor analysis section below in the full measurement and structural model. The estimation of this mod el was conducted in two steps similar to the full model to allow for the testing of the moderating effects of personal affinity for technology. Finally, to assess the increase in predictive validity of including the moderating effect over and above the in clusion of the direct effects of PCAFT and AFT, a series of nested regressions were run using the summated scale scores for each respondent as the predictors variables. In the first block, only the impact of PCAFT on service performance (SERVPERF) was meas ured. The second block added the direct influence of AFT of SERVPERF and the third block showed the interactive effect that would indicate a moderated relationship. The use of this method allowed for a detailed assessment of the incremental impact of each variable on the model fit (nested F test) as well as the predictive ability of the mo del (adjusted r squared value). Findings Confirmatory f actor a nalysis The confirmatory factor analysis supported the expected factor structure across all multi item sca les. As can be seen in T able 14 the

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124 loading of each indicator on its respective latent construct was significant at the p < .001 level. An examination of the modification indices of the model also revealed that cross loading indicators onto other latent c onstructs would not significantly improve the measurement model, suggesting that each indicator was only tapping into one of the latent constructs and providing further support for the proposed factor structure. The model fit statistics and infer factor co rrelations raise some measurement issues. First off, the model not only fails the chi square test of perfect fit for the population (as it commonly does when a study contains a large sample size), but it also does not meet the test of close fit that tests whether or not the root mean square error of approximation (RMSEA) is less than .08. The model RMSEA of .097 indicates that the model has marginal fit at best, and the other fit indices support this as well. The exception to this marginal fit finding is th e Goodness of Fit family of indices (i.e., GFI, AGFI, PGFI) which indicate that the measurement model has a poor fit. However, to judge the relative fit of this model a comparison was made using the same variable in different configurations and examining t he fit statistics. As can be seen in Table 14 the fit statistics of the proposed model are clearly better than the independence and single construct models, which indicates that this factor structure does a better job of explaining the data that models wh ere there is no factor structure or that all of indicators are related to a single latent construct respectively. When compared to the saturated model, the fit statistics for the model were obviously worse, but when parsimony and theory are taken into acco unt the proposed model becomes clearly better than a model in which every possible path is linking all observed and latent variable is suggested. An additional explanation for the poor model fit comes from the fact that the distribution of responses

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125 to eac h of the indicator variables was non normal. In almost every case the observed variables were negatively skewed, and even slight deviations from normality can have sever adverse effects on the results of the maximum likelihood estimation techniques used to fit this and the other SEM models in this paper. The inter factor correlations raise the issue of some kind of measurement artifact as a potential confounding factor in the measurement model and the poor overall fit results. While the expected inter facto r correlations between the constructs of interest (AFT, PCAFT and Service Performance) were in the directions and magnitudes expected (see Table 14 ), the correlations between the marker variable of Fashion Conscientiousness and the constructs of interest w ere significant as well. In the cases of PCAFT and Service Performance (SERVPERF) the correlations are not a major indicator of a measurement issue as the magnitudes are low and the significance is the result of the large sample size. T he correlation betw een A F T and Fashion Conscientiousness is a cause for concern as the magnitude of this correlation between two variables that should not be related is higher than the correlation between AFT and Service Performance that are expected to be indirectly related and should show some correlation. This indicates that there may be an issue with common method bias or other measurement artifacts (i.e., response sets ) affecting the measurement of a ffinity for technology.

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126 Table 14. Confirmatory Factor Analysis Results Confirmatory Factor Analysis Indicator Lambda Theta Sig. Stdzd Weight Latent Construct: Affinity for Technology AFT1 1.000 NA NA 0.719 AFT2 0.856 0.064 p < .001 0.715 AFT3 1.085 0.075 p < .001 0.780 AFT4 1.142 0.071 p < .001 0.862 AFT5 1.168 0.06 9 p < .001 0.899 AFT6 0.883 0.071 p < .001 0.672 AFT7 1.200 0.069 p < .001 0.924 AFT8 1.180 0.070 p < .001 0.897 AFT9 1.133 0.071 p < .001 0.849 AFT10 1.046 0.072 p < .001 0.774 Latent Construct: Perceived Co rporate Affinity for Technology PCAFT1 1. 000 NA NA 0.861 PCAFT2 1.079 0.046 p < .001 0.887 PCAFT3 1.087 0.048 p < .001 0.876 PCAFT4 1.145 0.042 p < .001 0.948 PCAFT5 1.154 0.040 p < .001 0.972 PCAFT6 1.112 0.043 p < .001 0.932 PCAFT7 1.144 0.045 p < .001 0.92 PCAFT8 1.137 0.043 p < .001 0. 939 Latent Construct: Service Performance RESP1 1.000 NA NA 0.842 RESP2 1.162 0.046 p < .001 0.944 RESP3 1.124 0.045 p < .001 0.937 EMP1 1.058 0.044 p < .001 0.917 EMP2 1.209 0.050 p < .001 0.926 EMP3 1.184 0.050 p < .001 0.917 REL1 1.072 0.047 p < .001 0.893 REL2 1.121 0.051 p < .001 0.875 REL3 1.073 0.054 p < .001 0.833 TAN1 0.929 0.046 p < .001 0.832 TAN2 0.899 0.044 p < .001 0.838 TAN3 0.892 0.042 p < .001 0.858 ASU1 1.129 0.046 p < .001 0.929 ASU2 1.151 0.047 p < .001 0.927 ASU3 0.917 0 .044 p < .001 0.857 Latent Construct: Fashion Conscientiousness FC1 1.000 NA NA 0.679 FC2 1.253 0.097 p < .001 0.879 FC3 1.151 0.089 p < .001 0.824

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127 Table 14. Confirmatory Factor Analysis Results (Continued) Confirmatory Factor Analysis Inter factor Covariances and Correlations Latent Constructs Covariance Stnd. Error Sig. Correlation AFT > PCAFT 0.261 0.062 < .001 0.248 AFT > SERVPERF 0.162 0.054 0.003 0.170 AFT > FC 0.417 0.083 < .001 0.339 PCAFT > SERVPERF 0.543 0.057 < .001 0.709 PCAFT > FC 0.152 0.059 0.010 0.155 SERVPERF > FC 0.107 0.053 0.042 0.120 Model Fit Fit Statistic Proposed Saturated Independence Single Const. RMSEA 0.097 NA 0.275 0.194 Low 90% CI 0.093 NA 0.272 0.190 High 90% CI 0.101 NA 0.279 0.198 PCLOSE 0.000 NA 0. 000 0.000 RMR 0.079 0.000 0.652 0.420 GFI 0.681 1.000 0.103 0.275 AGFI 0.639 NA 0.052 0.188 PGFI 0.602 NA 0.097 0.246 NFI 0.854 1.000 0.000 0.513 PNFI 0.797 0.000 0.000 0.484 RFI 0.843 NA 0.000 0.484 IFI 0.884 1.000 0.000 0.532 TLI 0.875 NA 0.000 0.502 CFI 0.884 1.000 0.000 0.531 PCFI 0.825 0.000 0.000 0.500 ECVI 7.690 3.828 49.731 24.517 Structural e quation m odels Two different structural models were estimated and compared for fit. The first model did not include the interaction term while the second did. The non interactive model showed marginal fit as indicated by the statistics in Table 15 Most notable, the RMSEA of the model was higher than acceptable while the standard fit indices were lower than desired. These same issues of fit also affect the

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128 interactive model with the excepti on that the RMSEA was closer to the target maximum of .08. W hen both models are compared to saturated and independence model, it is clear that both the non interactive and interactive models are preferable to mo dels that suggest all possible paths or minimal paths based on a comparison of the fit indices. And over all it appears that the interactive model is the better of the two proposed models. There are a couple of potential reasons these poor results. The fi rst is that these models were full SEM models, so the issues that arose in the CFA may be harming the fit of the entire model. To clarify this potential problem with the full SEM model, parcel models were also tested and will be discussed in the next secti on. The second is that this model does not include any of the other antecedents of service performance that have been found in the literature. Therefore, the variance accounted for in this outcome is limited by the inclusion of only one possible anteceden t in the model. Also, again the fact that the observed variables were negatively skewed may also be a contributing factor to the less than stellar fit of this model. An examination of the structural paths for both proposed models (see Table 15 ) show that the path from perceived corporate affinity for technology to service performance is strong and positive as hypothesized. Additionally, both models show no performance. Th e interaction between PCAFT and AFT that would indicate a moderating affinity for technology and service performance is not significant, but is stronger than the link between affinity for technology on service performance and is close to the necessary .10 significance level that would indicate the expected moderating effect. Because this

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129 path is close the question becomes whether the noise in the measurement portion of the model is obscuring these findings, thus parcel models and nested regression models were fitted to the data to see if this could be further teased apart. The paths from SERVPERF to subjective disconfirmation, subjective disconfirmation to satisfaction and satisfaction to word of mouth intentions were all strong and positive as hypothesized. Table 15. Structural Equation Model Results Model Fit Statistics Non Interactive Interactive Saturated Independence Chi square 2678.327 2711.983 N/A N/A df 59 1.000 624.000 N/A N/A P value 0.000 0.000 N/A N/A RMSEA 0.101 0.098 N/A 0.277 90% CI Low 0.097 0.094 N/A 0.273 90% CI High 0.105 0.102 N/A 0.280 RMR 0.077 0.077 0.000 0.653 GFI 0.672 0.674 1.000 0.090 AGFI 0.630 0.633 N/A 0.040 PGFI 0.596 0.598 N/A 0.085 NFI 0.854 0.853 1.000 0.000 PNFI 0.801 0.799 0.000 0.000 RFI 0.844 0.843 N/A 0.000 IFI 0.882 0.883 1.000 0.000 TLI 0.874 0.874 N/A 0.000 CFI 0.882 0.882 1.000 0.000 PCFI 0.827 0.827 0.000 0.000 NCP 2 087.327 2 087.983 0.000 18 202.305

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130 Ta ble 15. Structural Equation Model Results (Continued) Structural Paths Non Interaction Model Interaction Model Path Regression Weight Sig Regression Weight Sig PCAFT > SERVPERF 0.643 < .001 0.622 < .001 AFT > SERVPERF 0.004 0.893 0.001 0.985 PC AFTxAFT > SERVPERF N/A N/A 0.042 0.142 SERVPERF > SubDis 0.853 < .001 0.853 < .001 SubDis > Global Sat 0.853 < .001 0.853 < .001 Global Sat > W o M Intent 0.968 < .001 0.968 < .001 Parcel m odels The parcel models were analyzed in order to gene rate a clearer understanding of the structural relationships without the previously noted noise inherent in the path models. Again the customer AFT and PCAFT indicators were mean centered to reduce the multicolinearity between them and the interaction term They were then summed and averaged to create a scale score for each respondent and then these two scale scores were multiplied to create a measure of interaction. These three terms were then entered into a structural model as observed exogenous variables along with the average scale score for each respondent on service performance and the observed variables used to measure subjective disconfirmation, satisfaction and word of mouth intentions as endogenous variables. A second model was tested with the inte raction term removed. As can be seen in Table 16 the results of these models also have some fit issues such as high RMSEA values and low parsimony adjusted fit indices. In this case most of the fit issues come from the fact that the variables used to crea te the scale score were non normal in their distribution and this trait was magnified when they were combined, and, as noted previously, this has a negative impact on the results of the

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131 maximum likelihood estimation technique used in the model. A compariso n of the interaction and non interaction models with saturated and independence models again reveal that either of the two proposed models would be a better choice than a model that proposes every possible path and a model the proposes very few paths. Base d on the RMSEA, root mean residual (RMR) and the raw fit indices; the interaction model appears to be t he best fit for this data set. The structural paths in the interaction and non interaction model (see Table 16 ) confirm the findings of the full SEM mod el that the path from PCAFT to SERVPERF is strong and in the positive direction again supporting Hypothesis 1. The non interaction model also shows that the direct effect of affinity for technology on perceptions of service performance is not significant. The interaction model does show that personal affinity for technology does moderate the relationship between perceived corporate affinity for technology and service performance perceptions at the .10 level of significance. This finding is different from th e full SEM model that contained the interaction term and does give some credence to the idea that the noise in the measurement model was masking some of the interaction effect. The rest of the paths also serve to reinforce the findings of the full SEM mode l, namely that the paths from SERVPERF to subjective disconfirmation, subjective disconfirmation to satisfaction and satisfaction to word of mouth intentions are all strong and positive as hypothesized.

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132 Table 16. Parcel Model Results Model Fit Fit Stati stic Interaction Non Interaction Saturated Independence RMSEA 0.151 0.177 N/A 0.496 LO 90 0.125 0.147 N/A 0.477 HI 90 0.178 0.207 N/A 0.515 RMR 0.053 0.059 0.000 0.433 GFI 0.930 0.920 1.000 0.366 AGFI 0.837 0.813 N/A 0.154 PGFI 0.399 0.394 N/A 0.274 NFI 0.941 0.940 1.000 0.000 PNFI 0.538 0.564 0.000 0.000 RFI 0.897 0.899 N/A 0.000 IFI 0.947 0.944 1.000 0.000 TLI 0.907 0.907 N/A 0.000 CFI 0.947 0.944 1.000 0.000 PCFI 0.541 0.566 0.000 0.000 ECVI 0.400 0.375 0.161 5.264 Structural Paths Mod el: Interaction Non Interaction Path: Regression Weight Sig. Regression Weight Sig. PCAFT > SERVPERF 0.582 < .001 0.607 < .001 AFT > SERVPERF 0.001 0.969 0.003 0.921 PCAFTxAFT > SERVPERF 0.047 0.059 N/A N/A SERVPERF > Subjective Disconfirmation 0.777 < .001 0.777 < .001 Subjective Disconfirmation > Satisfaction 0.853 < .001 0.853 < .001 Satisfaction > Word of Mouth Intentions 0.967 < .001 0.967 < .001 Nested r egressions The purpose of the nested regression analysis was to examine the incr emental increase in explanatory power of the model through the addition of the interaction term. This analysis focused on the first half of the model To utilize this procedure, the path from PCAFT to s ervice p erformance was estimate and then the path

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133 from AFT to S ERV P ERF was added to the model and finally the interaction of PCAFT and AFT on s ervice performance was added This allowed for a comparison of the changes to the model with the addition of each component. The findings here are similar to those of the other models in that the path from PCAFT to SERVPERF is strong and positive while the addition of a direct effect of AFT on SERVPERF does little for the model and the addition of the interaction term does improve the model. As can be seen in Table 17 t he interaction term is significant at the .10 level of significance and does improve the model significantly according to the nested F test while increasing the percentage of variance in SERVPERF explained by the model by .3 percent. Regression was also us ed to test the additional links in the model, which were again to found to be significant and extremely powerful when it came to explaining the varia nce in each of the outcomes. Table 17. Nested Regression Findings Model 1 Model 2 Model 3 Path Beta Si g Beta Sig Beta Sig PCAFT > SERVPERF 0.700 0.000 0.701 0.000 0.894 0.000 AFT > SERVPERF N/A N/A 0.004 0.921 0.339 0.070 PCAFTxAFT > SERVPERF N/A N/A N/A N/A 0.439 0.061 Model F value 333.320 0.000 166.190 0.000 112.790 0.000 Nested F value N/A N/ A 0.001 NS 3.544 p<.10 Adjusted R square 0.488 0.487 0.491 SERVPER > SubDis 0.751 0.000 Model F value 448.230 0.000 Adjusted R square 0.562

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134 Table 17. Nested Regression Findings (Continued) Model 1 Model 2 Model 3 SubD is > Global Sat 0.865 0.000 Model F value 1031.260 0.000 Adjusted R square 0.748 Global Sat > W o M Intent 0.914 0.000 Model F value 1757.100 0.000 Adjusted R square 0.835 Discussion/ Limitations The findings from t he above models all point to the same conclusions. The first is affinity for technology which supports Hypothesis 1, and shows that PCAFT is indeed a signal to customers about the service quality of the firm. The second is that this his/her perception influence how strong that signal is when it comes to assessing service performance. While the rest of the hypotheses (H 3 H 5 ) were confirmed as would be expected of relationships that have been so robustly found in the literature; the fact that they were demonstrated through the use of single item measures and that the results were as strong if not stronger than those found with multi item scales gives additional support to the findings of Wanou s Reichers, and Hudy (1997) of the viability of this type of measure in certain situations.

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135 Table 18. Hypothesis Outcomes Hypothesis Supported H1: Customer Perceived Corporate Affinity for Technology is positively related to customer rating of service performance. Yes H2: Customer personal affinity for technology moderates the relationship between Customer Perceived Corporate Affinity for Technology and customer ratings of service performance. Yes H3: Service performance is positively related to subje ctive disconfirmation. Yes H4: Subjective disconfirmation is positively related to global satisfaction with the firm. Yes H5: Global satisfaction with the firm is positively related to customer word of mouth intentions. Yes These finding must be taken cautiously as there are several limitations to them. The first is that the measurement model was a bit clouded due to the unexpected correlation between AFT and Fashion Conscientiousness due to some sort of measurement artifact, which means that the findi ngs regarding the moderation role of Affinity for technology are not as clear as they could be. Alternatively, this could be taken as a conservative test of the influence of AFT given that the measurement artifact should have made it more difficult to dete ct any moderating effect if one did exist. A second limitation is the fact that the model fit for the SEM and Parcel Models were not great which indicates that there may be a better model out there that was not tested. This is a reasonable short coming giv en that this model only includes one antecedent of service performance and there are many other antecedents that have been shown to be strong drivers of customer ratings of this measure (Carrillat Jaramillo, and Mulki, 2007). The exclusion of these other variables means that much of the ability to explain the

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136 variance in service performance due to other factors is missing and there for the model does not fit perfectly. Future research should be conducted to determine exactly how important PCAFT is to custo mer perceptions of service performance relative to other established antecedents. A third limitation is that this data set was collected in the lobbies of bank branches while researching the use of technology, and that this sample may not include the more technologically inclined customers who may However, this limitation also makes the current study a conservative test of the model as the exclusion of these types of customers would make the direct and moderating effects harder to find. A final limitatio n of this study is the fact that the data is collected in a single industry and specifically a single firm within that industry. Future research should be undertaken to determine if these results extend beyond both this firm and the financial services indu stry. Implications Academic The findings of this paper have several important implications for academicians. The first is that this paper introduces a new class of potential antecedents to the formation of customer perceptions of service outcomes. Speci fically, this paper shows that perceived firm attitudes my influence customer perceptions of service quality, and through service quality perceptions indirectly influence other key outcomes of interest to the firm. The second key finding is that the congru ity between individual attitude and perceived firm attitude determines strength of this antecedent relationship. While this may not seem like a big contribution given the extensive literature on congruity theory, it is actually very important as it shows t hat the influence of congruity extends beyond the match of customer and firm traits (usually personality traits). This

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137 study shows that congruity theory should be expanded to include the agreement between customer personal attitudes and their perception of firm attitudes as well. Finally, this paper begins the process of developing the nomological network for the new construct of perceived corporate affinity for technology. Given that this is a new construct with very little empirical research, it is import ant that it be thoroughly tested in order to assess its convergent, discriminant, concurrent and predictive validity. It is also important to test this new construct to determine what, if any, influence it will have on the curren t knowledge base of the fie ld. M anagerial For managers, this paper contains some important insights as well. First, the study shows the importance of customer perceptions of firm attitudes, in this case toward technology, but it could reasonably be extended to customer perceptions of firm attitudes toward objects, ideas or causes. As shown in the Wal Mart example above, it seems that the management of firms already suspects this, but this paper provides empirical evidence of the importance of customer perceptions of firm attitudes a nd links these perceptions to their impact on key outcomes that relate directly to customer attraction, retention and profitability. This provides managers with evidence to present to their shareholders in defense of their efforts to project certain attitu des to customers. A second benefit that this paper provides managers is that it shows the importance of enhance or limit the strength of relationship between custome r perceptions of firm and firm do not have favorable personal attitudes toward technology, then the efforts to echnology are frivolous at best and

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138 harmful at worst. Thus, it provides empirical support for managers of the importance of attempt to project through the firm. Finally, this paper shows managers the importance of determine which firms survive or even thriv e.

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139 References Aaker, J L. (1997). Dimensions of b rand p ersonality Journal of Marketing 34 (3), 347 356. Boulding, W & Kirmani A. (1993) A c onsumer s ide e xperimental e xamination of s ignaling t heory: Do c onsumers p erceive w arranties as s ignals o f q uality? The Journal of Consumer Research 20 (1), 111 123. Brady, M K., Cronin, Jr. J. J., & Brand R. R. (2002) Performance Only m easurement of s ervice q uality: A r eplication and e xtension Journal of Business Research 55 (1), 17 31. Brosnan, M J. 1998. The i mpact of c omputer a nxiety and s elf e fficacy upon p erformance Journal of Computer Assisted Learning 14 (3), 223 234. Brown, D. E. (1991) Human u niversals New York, NY: McGraw Hill. Brown, T. J., Barry, T. E., Dacin P. A ., & Gunst R. F. (2 005). Spreading the w ord: i nvestigating a ntecedents of c p ositive w ord of m outh i ntentions and b ehaviors in a r etailing c ontext Journal of the Academy of Marketing Science 33 (2), 123 138. Burch, E Rogers H. P., & Underwood III J. (1995). E xploring SERVPERF: An e mpirical i nvestigation of the i mportance p erformance, s ervice q uality r elationship in the u niform r ental i ndustry A vailable at http://sbaer.uca.edu/research/swma/1995/pdf/17.pdf Carrillat, F. A., Jaramillo F., & Mulki J. P. (200 7). The v alidity of the SERVQUAL and SERVPERF s cales: A m eta a nalytic v iew of 17 y ears of r esearch a cross f ive c ontinents International Journal of Service Industry Management 18(5), 472 490. Churchill, G A. (1979) A p aradigm for d eveloping b etter m eas ures of m arketing c onstructs Journal of Marketing Research 16 (1), 64 73. Churchill, Jr., G A. & Surprenant C. (1982). An i nvestigation into the d eterminants of c ustomer s atisfaction Journal of Marketing Research 19 (4), 491 504.

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140 Cronin, J r J J. & Taylor S. A. (1992) Measuring s ervice q uality: A r eexamination and e xtension Journal of Marketing 56 (3), 55 68. Curran, J. M. & Meuter M. L. (2005). Self service t echnology a doption: Comparing t hree t echnologies Journal of Services Marketing 19 (2) 103 113. Curran, J M. & Meuter M. L. (2007) Encouraging e xisting c ustomers to s witch to s elf s ervice t echnologies: Put a l ittle f un in their l ives The Journal of Marketing Theory and Practice 15 (4), 283 298. Curran, J. M., Meuter M. L., & Suprenan t C. F. (2003). Intentions to u se s elf s ervice t echnologies: A c onfluence of m ultiple a ttitudes Journal of Service Research 5 (3), 209 224. d'Astous, A. & Levesque M. (2003) A s cale for m easuring s tore personality. Psychology and Marketing 20(5), 455 469. Day, R. L. (1984) Modeling c hoices a mong a lternative r esponses to d issatisfaction I n T C. Kinnear, E d. Advances in c onsumer r esearch Ann Arbor, MI: Association for Consumer Research, 496 499. Edison, S. W. & Geissler G. L. (2003). Measuring a t titudes t owards g eneral t echnology: Antecedents, h ypotheses and s cale d evelopment Journal of Targeting, Measurement and Analysis for Marketing 12 (2), 137 156. Ekinci, Y. & Riley M. (2003). An i nvestigation of s elf c oncept: Actual and i deal s elf c ongrue nce c ompared in the c ontext of s ervice e valuation Journal of Retailing and Consumer Services 10 (4), 201 14. Fleming, D. & Artis A. B. ( forthcoming ) Measuring c orporate a ffinity for t echnology: A s cale for c ustomers and e mployees Journal of Personal S elling and Sales Management e lling: Threats and Geissler, G L. & Edison S. W. (2005) Market m a ttitudes t owards g eneral t echnology: Implications for m arketing c ommunications Journal of Market ing Communications 11 (2), 73 94. George, W R., Kelly J. P. & Marshall C. E. (1986). The s elling of s ervices: A c omprehensive m odel Journal of Personal Selling & Sales Management 6 (2), 29 37. Goldman, R. D., Platt B. B., & Kaplan R. B. (1973) Dim ensions of a ttitudes t oward t echnology Journal of Applied Psychology 57 (2), 184 187.

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141 Gunther, M. (2006). Wal Mart s ees g reen CNNMoney.com. http://money.cnn.com/2006/07/25/news/companies/wal mart short.fortune/ Harris, E G. & Fleming D. E. (2005) A ssessing the h uman e lement in s ervice p ersonality f ormation: Personality c ongruency and the f ive f actor m odel Journal of Services Marketing 19 (4), 187 198. Heckman, R. and Guskey A. (1998). The r elationship b etween a lumni and u niversity: Toward a t heor y of d iscretionary c ollaborative b ehavior Journal of Marketing Theory and Practice 6 (2), 97 112. Heinssen, R. K., Glass C. R., Knight L. A. (1987). Assessing c omputer a nxiety: D evelopment and v alidation of the c omputer a nxiety r ating s cale Computers in Human Behavior 3 (1), 49 59. Heskett, J. L., Jones, T. O., Loveman, G. W., Sasser Jr., W. E., & Schlesinger L. (1994). Putting the s ervice p rofit c hain to w ork Harvard Business Review 72 (2), 164 1 74. Honebein P C & Cammarano R. F. (20 06). Cust omers at w ork Marketing Management 15(1), 26 31. Ippolito, P. M. (1990) Bonding and n onbonding s ignals of p roduct q uality The Journal of Business 63 (1), 41 60. Jarvis, C B MacKenzie S. B., & Podsakoff P. M. (20 03). A c ritical r eview of c onstruc t i ndicators and m easurement m odel m isspecification in m arketing and c onsumer r esearch Journal of Consumer Research 30 (2), 199 218. Lau, K C & Phau I. (2007) Extending s ymbolic b rands u sing t heir p ersonality: Examining a ntecedents and i mplications t owards b rand i mage f it and b rand d ilution Psychology and Marketing 24(5), 421 444. Lindell, M K. & Whitney D. J. (2001) Accounting for c ommon m ethod v ariance in c ross s ectional r esearch d esigns The Journal of Applied Psychology 86 (1), 114 121. Lum pkin, J. R. & Darden W. R. (1982). Relating t elevision p reference v iewing to s hopping o rientations, l ife s tyles and d emographics: The e xamination of p erceptual and p reference d imensions of t elevision p rogramming Journal of Advertising 11 ( 4 ), 56 67. Mac Kenzie, S. B. (2003) The d angers of p oor c onstruct c onceptualization Journal of the Academy of Marketing Science 31 (3), 323 326. McGill, A. L. (2000) Counterfactual r easoning in c ausal j udgments: Implications for m arketing Psychology & Marketing 17 ( 4), 323 343.

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142 Milgrom, P. & Roberts J. (1986) Price and a dvertising s ignals of p roduct q uality The Journal of Political Economy 94 (4), 796 821. Murray, K. B. (1991) A t est of s ervices m arketing t heory: Consumer i nformation a cquisition a ctivities Jou rnal of Marketing 55(1), 10 25. Nelson P. (1970) Information and c onsumer b ehavior J ournal of Political Economy 78(2), 311 329. Oliver, R. L. (1980) A c ognitive m odel of the a ntecedents and c onsequences of s atisfaction d ecisions Journal of Marketin g Research 17 (4), 460 469. Oliver, R. L. (1997) Satisfaction: A b ehavioral p erspective on the c onsumer Boston MA : McGraw Hill. Oliver, R L. & Bearden W. O. (1985). Disconfirmation p rocesses and c onsumer e valuations in p roduct u sage Journal of Busi ness Research 13 (3), 235 246. Oliver, R L. & Swan J. E. (1989). Equity and d isconfirmation p erceptions as i nfluences on m erchant and p roduct s atisfaction The Journal of Consumer Research 16 (3), 372 383. Olson, J. C. & Dover P. (1976). Disconfirmati on of c onsumer e xpectations t hrough p roduct t rial Journal of Applied Psychology 64 (2), 179 198. Parasuram an, A. (2000). Technology r eadiness i ndex (TRI): A m ultiple i tem s cale to m easure r eadiness to e mbrace n ew t echnologies Journal of Service Research 2 (4), 307 320. Parasuraman, A., Zeithaml V. A., & Berry L. L. (1985). A c onceptual m odel of s ervice q uality and i ts i mplications for f uture r esearch Journal of Marketing 49 (4), 41 50. Parasuraman, A., Zeithaml V. A., & Berry L. L. (1988). SERVQUA L: A m ultiple item s cale for m easuring c onsumer p erceptions of s ervice q uality Journal of Retailing 64(1), 13 40. Parasuraman, A., Zeithaml, V. A., & Berry L. L. (1994). Reassessment of e xpectations as a c omparison s tandard in m easuring s ervice q uality : Implications for f urther r esearch Journal of Marketing 58 (1), 111 124. Pearson, G. & Young A. T. (2002). Technically s peaking: Why a ll Americans n eed to k now m ore about t echnology National Academy of Engineering, Washington, D.C.: National Academy P ress

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143 Peter, J. P Churchill Jr. G. A., & Brown T. J. (1993). Caution in the u se of d ifference s cores in c onsumer r esearch Journal of Consumer Research 19(4), 655 662. Ping, R A. (1996). Latent v ariable r egression: A t echnique for e stimating i ntera ction and q uadratic c oefficients Multivariate Behavioral Research 31 (1), 95 120. Podsakoff, P. M. & Organ D. W. (1986). Self Reports in o rganizational r esearch: Problems and p rospects Journal of Management 12 (4), 531 544. Ridnour, R E., Lassk F. G & Shepherd C. D. (2001) An exploratory assessment of sales culture variables: Strategic implications within the banking industry Journal of Personal Selling & Sales Management 21 (3), 247 254. Rossiter, J. R. (2003) The C OAR SE procedure for scale development in marketing International Journal of Research in Marketing 19 (4), 305 335. Rust, R T. & Zahorik A. J. (1993) Customer s atisfaction, c ustomer r etention, and m arket s hare Journal of Retailing 69 (2), 193 215. Schneider, B & Bowen D. E. (1999). Understanding c ustomer d elight and o utrage Sloan Management Review 41 (1), 35 45. Segars, A. H. (1997). Assessing the u nidimensionality of m easurement: A p aradigm and i llustration within the c ontext of i nformation s ystems r esearch Omega 25 (1), 107 121. Sheth, J N. (1971) Word of Mouth in l ow r isk i nnovations Journal of Advertising Research 11 (3), 15 18. Simpson, R D. & Troost K .M. (1982). Influences on c ommitment to and l earning of s cience a mong a dolescent s tudents Science Education 66 (5), 763 781. Sirgy, M. J (1980). Self Concept in r elation to p roduct p reference and p urchase i ntention I n V. V. Bellur E d. Developments in m arketing s cience Vol. 3, Marquette, MI: Academy of Marketing Science, 350 354. Sirgy, M. J (1981 ) Testing a s elf c oncept m odel u sing a t angible p roduct I n Proceedings of the American p sychological a ssociation c onsumer p sychology d ivision 89. Sirgy, M. J (1982a) Self Image/ p roduct i mage c ongruity and a dvertising s trategy I n Vinay Kothari, Ed. Developme nts in m arketing s cience Vol. 5, Marquette, MI: Academy of Marketing Science

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144 Sirgy, M. J (1982b) Self Image/ p roduct i mage c ongruity and p urchase m otivation: A r ole p laying e xperiment Proceedings of the American Psychological Association Consumer Psyc hology Division 90. Sirgy, M. J (1982c) Self concept in c onsumer b ehavior: A c ritical r eview The Journal of Consumer Research 9(3), 287 300. Sirgy, M. J & Danes J. (1981) Self Image/ p roduct i mage c ongruence m odels: Testing s elected m athematical m odels I n Andrew Mitchell Ed., Advances in c onsumer r esearch Vol. 9, Ann Arbor, MI: Association for Consumer Research, 556 561. Sirgy, M. J Johar, J. S., Samli A. C., & Claiborne C. B. (1991). Self congruity v ersus f unctional c ongruity: Predictors o f c onsumer b ehavior Journal of the Academy of Marketing Science 19(4), 363 375 Sirgy, M. J & Samli A. C. (1985). A p ath analytic m odel of s tore l oyalty i nvolving s elf concept, s tore i mage, g eographic l oyalty, and s ocio economic s tatus Journal of the Academy of Marketing Science 13 (3), 265 291. Spence, A. M (1974) Market s ignaling: Informational t ransfer in h iring and r elated s creening p rocesses Cambridge, MA: Harvard University Press. Swan, J. E. & Trawick I. F. (1981). Disconfirmation of e xpe ctations and s atisfaction with a r etail s ervice Journal of Retailing 57 (3), 49 67. Tse, D. K. & Wilton P. C. (1988). Models of c onsumer s atisfaction f ormation: An e xtension Journal of Marketing Research 25 (2), 204 212. Wanous, J. P., Reichers A. E. & Hudy M. J. (1997). Overall j ob s atisfaction: How g ood are s ingle i tem measures? Journal of Applied Psychology 82 (2), 247 2 52. Westbrook R A. (1987) Product/ c onsumption b ased a ffective r esponses and p ostpurchase p rocesses Journal of Marketing Rese arch 24 (3), 258 270. Woodall, R D., Colby C. L., & Parasuraman A. (2007). E volution to r evolution: Capitalize on the i mminent e ra of e xplosive e s ervices g rowth Marketing Management 16 (2), 29 34.

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145 Appendix 3 : Customer Survey Items Affinity f or Technology (Edison and Geissler 2003) 1. I enjoy learning new computer programs and hearing about new technologies 2. If I am given an assignment that requires that I learn to use a new program or how to use a machine, I usually succeed. 3. Solving technological problems seems like a fun challenge 4. Technology is my friend 5. I find most technology easy to learn 6. People 7. I relate well to technology and machines 8. I am comfortable learning new technology 9. I know how to deal with technological malfunctions or problems 10. I feel as up to date on t echnology as my peers. Perceived Corporate Affinity for Technology (pretest) 1. BANK X views technology as a friend 2. BANK X offers t he latest technologies . 4. BANK X seems comfortable implementing new technology 5. BANK X relates well to technology 6. BANK X knows how to deal with technological problems 7. I feel BANK X is as up to date on technology as its competitors 8. BANK X shows its relationship with technology by offering secure technology based services.

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146 SERVPERF (Harris and Fleming 2005) Please rate your agreement with the following statement s about BANK X Reliability 1) Complete services when promised 2) Insists on error free service 3) Performs services right the first time Assurance 1) Instills confidence in customers 2) Makes you feel safe and secure 3) Is up to date on banking knowledge Tangibles 1) Has pleasant facilities 2) Has visually appealing materials . Empathy 1) Gives individual attention 2) Cares about your specific needs 3) Keeps custo mers interests in mind Responsiveness 1) Provides prompt service 2) Is responsive to your needs 3) Always willing to help you Global Satisfaction (Harris and Fleming 2005) = Extremely Satisfied) Subjective Disconfirmation (Harris and Fleming 2005) Compared to your overall expectations, how do you perceive BANK X Word of Mouth Intentions (Harris and Fleming 2005) How likely is it that you would recommend BANK X Fashion Conscientiousness (Lumpkin and Darden 1982) Please rate your agreement with how wel l the following statements describe you ( = Strongly When I must choose between the two, I usually dress for fashion, not for comfort An important part of my life and activities is dressing smartly A person should try to dress in style

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147 Demographics Please circle the answer that best describes you for each question below. How often do you conduct transactions with Bank X ? 3 or more times a week 1 2 times a week 1 2 times a month 6 10 times a year 3 5 times a year 2 or fewer times a year Please circle your gender: Male Female How long have you been a Bank X customer? ________Years _______Months How would you describ e the services you use at Bank X? Mostly personal Mostly commercial Mostly investments A mix of all Other (please describe) __________________ _____ Please indicate what percent of your banking transactions are completed by each of these methods Branch lobby ____% Drive through ____% ATM ____% Telephone ____% Online ____% Total 100 % Please circle the age range that best describes you: Under 20 20 29 30 39 40 49 50 59 60 69 70 79 80 or older

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148 About the Author in Business fro m the University of South Florida in 2002, M.B.A. in Marketing and Finance from the University of South Florida Polytechnic in 2005 and his Ph.D. from University of South Florida in 2010. He was named the 2009 Doctoral Fellow for the National Conference in Sales Management, the 2008 University of South Florida College of Business Ph.D. Student Research Award Recipient, and the 2008 University of South Florida Graduate and Professional Student Council Graduate Student Achievement Award Recipient. His researc h has been published in the Journal of Services Marketing, Service Marketing Quarterly, Health Marketing Quarterly and the Journal of Personal Selling and Sales Management.