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Consumers’ Behavioral Intentions Toward Self-Service Technology in the Emerging Markets a

Gurjeet Kaur & Sangeeta Gupta a

a

Department of Commerce, University of Jammu, Jammu, India

To cite this article: Gurjeet Kaur & Sangeeta Gupta (2012): Consumers’ Behavioral Intentions Toward Self-Service Technology in the Emerging Markets, Journal of Global Marketing, 25:5, 241-261 To link to this article: http://dx.doi.org/10.1080/08911762.2012.757406

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Journal of Global Marketing, 25:241–261, 2012 c Taylor & Francis Group, LLC Copyright  ISSN: 0891-1762 print / 1528-6975 online DOI: 10.1080/08911762.2012.757406

Consumers’ Behavioral Intentions Toward Self-Service Technology in the Emerging Markets

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Gurjeet Kaur Sangeeta Gupta

ABSTRACT. Self-service technologies (SSTs) allow customers to offer their own service encounters via the interaction of electronic service interfaces or machines rather than by interacting with a firm’s service personnel. This lack of personal interaction generates doubts and queries in the minds of the people, especially those unaware or less aware of these technology-based services. Such a situation is quite prevalent in the developing nations (like India), where still a large number of people are apprehensive about using the latest technologies. In this regard, the present study aims to develop an integrated model designed to predict and explain the various factors that influence customers’ behavioral intentions to use or not to use one particular SST (i.e., ATM services). The study finds that bank customers are less innovative and less optimistic to try out new technologies. Usefulness of the technology helps in developing positive attitude toward the technology, which in turn affects customers’ intentions to use that technology. KEYWORDS. Automated teller machine, technology readiness, technology acceptance model, behavioral intentions

INTRODUCTION Today’s fast-paced world is increasingly characterized by technology-facilitated transactions used to expedite a wide range of service encounters and to manage the relationship between the service provider and the customer (Curran, Meuter, & Surprenant, 2003; Grant & Schlesinger, 1995; McKenna, 1995; Peters, 1997). Technology requires more employees and customers to interact with technology-based systems either as a substitute for or as a complement to face-to-face service interactions (Curran et al., 2003; De Jong, de Ruyter, & Lemmink, 2003; Meuter, Ostrom, Roundtree, & Bitner, 2005).

Encouraging customers to use new technologies in service encounters is generally very challenging. One of the most complicated uses for technology has been the replacement of the firm’s employees in the delivery of services. No doubt, this use of technology has an extensive appeal to the service provider, as it can standardize service delivery, reduce labor costs, and expand the options for delivery; however, there can be a significant drain on resources if not widely accepted by customers (Curran & Meuter, 2005). Information and communication technology incorporation by the banks has changed the way in which banking is being done worldwide. These changes have been pioneered in India by

Gurjeet Kaur is a Senior Assistant Professor and Sangeeta Gupta is a Research Scholar, both in the Department of Commerce at the University of Jammu, Jammu, India. Address correspondence to Dr. Gurjeet Kaur, Department of Commerce, University of Jammu, Jammu, 180006 India. E-mail: [email protected] 241

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new private sector and foreign banks to reach a wider customer base, because they had limited number of branches. The public sector and the old private sector banks, which were following the traditional method of banking until a few years ago, have also realized the benefits that could be reaped through the introduction of technology in their day-to-day operations. Although they are acting late, they are now increasingly pursuing a technology-centric strategy in banking operations and services delivery as manifested by their adoption of core banking solutions and the introduction of technology-enabled banking solutions (Sambrani & Suryanarayana, 2007). Banks in India have, therefore, realized that technology strategy has become the cornerstone of their business strategy and that it provides new ways of affecting customer transactions and interactions (Godse, 2005).

Evolution of Delivery Channels in India Traditionally, banks in India relied extensively on their vast branch network to effectively reach customers and put emerging banks out of the competition. This was a high-cost strategy that involved the high expeditures on real estate and the banks operating costs and forced new banks to develop strategies that could help them reach end customers in more cost-effective ways. The solution came in the form of delivery channels such as automated teller machines (ATMs) and Internet banking. These channels turned out to be the growth drivers for private banks in India (Srikanth & Padmanabhan, 2002). With the infusion of technology into the banking systems, it is now possible for the banks to provide multiple delivery channels to provide banking products and services. In India, the traditional “brick and mortar” banks are complimenting their operations with “brick and click” strategies. The changed strategy regarding delivery channels is faced with extraordinary problems not known before. The new delivery channels such as ATMs, telephone banking, and Internet banking, along with better access to customer information, have reformed the relationship between banks and customers. Banks have the opportunity to market their products and services online, and additional

financial services like bank insurance can be targeted at the existing customers and prospects, thus facilitating customization to suit the needs of individual customers (Godse, 2005).

ATMs in India HSBC was the first bank to introduce the ATM concept in India in 1987. New private sector banks have taken the lead in introducing ATMs in a big way to supplement their branch network and to compete with large public sector banks with large branch networks. Industrial Credit and Investment Corporation of India (ICICI), Unit Trust of India (UTI), Housing Development Finance Corporation (HDFC), and Industrial Development Bank of India (IDBI) together accounted for more than 50% of the total ATMs in India. ICICI Bank was the first bank to cross the 1,000 mark in India (Thamaraiselvan & Raja, 2007). The current scenario has entirely changed with the banks in the public sector like SBI and its associates, Corporation Bank and Syndicate Bank, who are aggressively pursuing the installation of ATMs across the country. As of March 31, 2007, the total number of ATMs installed by various banks was 27,088, whereas in 2006, it was 21,147, registering a growth rate of 28% over the previous year. Nationalized banks constitute the largest share of installed ATMs, followed by the new private sector banks, State Bank of India (SBI) group, old private sector banks, and foreign banks. While new private sector banks and foreign banks have more off-site ATMs, nationalized banks, SBI group, and old private sector banks have more on-site ATMs. It is also worth noting that the number of ATM installations compared with the number of branches is 3.28 times greater for the new private banks and 3.5 times greater for the foreign banks. With the installation base of more than 27,000 ATMs (as of March 31, 2007) all over the country, ATMs are being used not only as cashdispensing machines (Mohanty, 2007) but also to pay electricity, telephone, cellular, and credit card bills, to pay insurance premiums, and to refill/recharge prepaid mobile phone accounts. In addition, Citibank and ICICI permit mutual funds transactions through ATMs. Citibank

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ATMs also let their customers place orders for demand drafts and fixed deposits. ICICI, IDBI, and SBI allow their customers to make donations to specific temples or charitable trusts through ATMs. Customers can also purchase a new Internet connection or buy renewal packs via ICICI ATMs, in addition to buying calling cards for domestic/overseas calls. SBI ATMs permit their customers to pay fees for select colleges at specified ATM centers, and IDBI ATMs even let its customers pay gas bills and subscription payments for select magazines. Apart from payment services, IDBI customers view news headlines, stock quotes, horoscopes, and movies running at theatres via ATMs (Israni, 2006). With the aggressive deployment of ATMs, enormous enhancement in productivity can be achieved as the banks in India are able to shift 50% to 80% of their respective cash transactions to this channel. This has resulted in a substantial cost savings for the banks, as the cost of transactions using ATM is only about 25% to 30% of the cost of branch transactions. The experience of The Federal Bank, a prominent old private bank, testifies to this fact as the new ATMs that were installed broke even within 6 months of their installation. The same bank shifted more than 60% of its cash transactions over this channel, even in rural areas, within 2 years of the introduction of their ATM networks (Nair, 2005). Despite a number of innovative services being made available at many ATMs, cash withdrawal remains the most accessed service. The migration of routine bank transactions like cash withdrawals and balance inquiries from teller counters to ATMs significantly raises the potential for savings in employee costs and greater employee focus on value-added revenue-enhancing activities such as selling other financial products and advisory services to customers. Due to the strong cash culture in India, cash deposits are most likely higher than in other markets, especially cash deposits made by commercial customers such as retail shopkeepers and those whose work involves substantial traveling. A high cash withdrawal rate results in higher ATM servicing costs due to frequent cash replenishment requirements. Recent developments in ATM technology have made it possible to recycle cash in ATMs. ATMs with a check deposit

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facility are not picking up in India; one of the reasons is the delay in collection of the checks deposited in ATMs. Checks deposited in ATMs are collected and then deposited in the designated branch for collection. Most utilities have an inadequate infrastructure for receiving bill payments, resulting in long queues at collection centers. Hence, bill payment at ATMs has not achieved noticeable acceptance by bank customers. Most banks provide this service through bilateral arrangements with billpayment service providers. ATMs are ideally suited to sell paper-based products and services such as tickets, wireless phone recharge cards, financial products, etc. The screen interface allows browsing and customization, access to bank accounts to facilitate payments, and printing capabilities to produce the actual product/service. A number of banks, including ICICI, SBI, and PNB, have ATMs at Mumbai’s local railway stations to dispense season tickets to commuters. Despite the stupendous growth in the banking sector in India during the past 10 years and the development of a highly competitive marketplace, comprising of both domestic and foreignbased banking entities in India, access to various banking services in India continues to remain a major problem (Agarwal, 2011). The access to banking is especially troublesome in rural and remote areas around the country. By the end of 2009, China had 210,000 ATMs and the United States had approximately 420,000. However, by the end of June 2011, India had only 75,000 ATMs. It seems fair to compare the number of ATMs in India with the numbers in China and the United States despite the fact that these latter two countries probably have a much better banking system in place. But, as the growth of India is often benchmarked against these very countries, it becomes a serious concern that the state of ATMs in India is not very impressive (Agarwal, 2011). Thus, it is imperative for service providers to understand how they can best design, manage, and promote new technologies to have the best chance of consumer acceptance (Curran & Meuter, 2005). Early research on technology adoption in Indian context has involved either cultural issues or the impact of the technology acceptance model (TAM; De Angeli,

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Athavankar, Joshi, Coventry, & Johnson, 2004) to understand the technology adoption behavior of customers in India; however, individualistic factors like optimism, innovativeness, and security issues also affect their decision to try and to accept new technologies, particularly self service technologies (SSTs), which have not been considered in previous studies. Therefore, this study attempts to investigate various factors that influence customers’ intentions to adopt ATM services and develops an integrated model for a conceptual framework to investigate ATM adoption and its related issues. The article is organized as follows: we first review the relevant literature on SST, the TAM, the Technology Readiness Index (TRI), service quality (SQ), attitude, and behavioral intentions. Next, conceptual framework and derived hypotheses are presented along with research methodology and findings. Finally, we conclude the study with managerial implications, limitations, and suggestions for further research.

REVIEW OF LITERATURE Self-Service Technologies The proliferation of technology used in service delivery has complicated the service encounter that was traditionally dominated by interpersonal interactions. Consumers are now faced with a myriad of technology-based service delivery options where they do not directly interact with service firm employees (Curran et al., 2003). These new technologies have been labeled as SSTs. With regard to SSTs, banks have to provide a satisfactory reply to the key question, To what extent could or should the personalized interface be removed from the frontline in banking? (Joseph et al., 1999). This general issue forms a part of the challenge faced by the financial service providers, which involves managing the balance among the staff’s new technology delivery platforms, branch networks, and customer preferences. The past academic focus on customer SSTs highlights the importance of exploring research issues where technology acts as a service enabler for the customers (Bitner, Brown, & Meuter, 2000; Gwinner, Gremler, &

Bitner, 1998). The type of technology interfaces include telephone-based technologies and various interactive voice response systems, direct online connections and Internet-based interfaces, interactive free-standing kiosks, and video or compact disc (CD) technologies (Meuter, Ostrom, Roundtree, & Bitner, 2000). The importance of SST in the retail environment has grown significantly during the past decade. Many retailers today are investing in SSTs to improve SQ and to cut costs (Weijters, Rangarajan, Falk, & Schillewaert, 2007). SSTs allow retailers to standardize their interaction with customers, which results in a more consistent service atmosphere independent of employees’ personality and mood (Hsieh, Yen, & Chin, 2004). In addition, SSTs allow consumers to be productive resources involved in the service delivery, thus allowing retailers to handle demand fluctuations without expensive adjustment of employees’ level (Curran et al., 2003). Therefore, SSTs are attracting a great deal of attention from academicians and practitioners because of their relative newness and strategic importance. The prevailing perception of an ATM is that of a tool providing a familiar functionality of basic financial information and dispensing cash (De Angeli, Lynch, & Johnson, 2002). The adoption has not been straightforward, requiring trust in the technology and willingness to modify behavioral strategies in the very sensitive domain of personal finance. In this regard, financial institutions have played a major, sometime coercive, role in encouraging ATM adoption. Various studies have been conducted to reveal the major drivers and deterrents of adoption and basic usability issues (Hatta & Iiyama, 1991; Little, Briggs, Coventry, & Knight, 2003; Mead & Fisk 1998; Pepermans, Verleye, & Cappellen, 1996). ATMs flourish within societies where time is precious and money is readily available.

Technology Readiness Index An important addition influencing adoption research was made by Parasuraman (2000), who developed the TRI as a framework identifying four dimensions of people’s attitude toward technology (optimism, innovativeness, discomfort,

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and insecurity). Empirical studies reveal that people’s belief about technology has both positive and negative facets and can be categorized into these four distinct technology readiness dimensions (Mick & Fournier, 1998; Parasuraman & Colby, 2001). Optimism refers to a positive view of technology and belief in the benefits of technology in increasing job efficiency and enhancing people’s lives at work and at home. Innovativeness refers to the extent to which a person believes that he or she is a thought leader and at the forefront of trying new technologybased products or services (i.e., the tendency to be a technology pioneer and thought leader). Discomfort consists of perceptions of lack of control over technology, a feeling of being overwhelmed by it and lack of confidence in making the technology work. Insecurity involves distrust of technology and skepticism about its ability to work properly. It is a need for assurance that a technology-based product or service or process will operate reliably and accurately. The first two TRI dimensions, “optimism” and “innovativeness,” are the contributors that can increase an individual’s technology readiness, whereas the other two dimensions, “discomfort” and “insecurity,” are inhibitors that can suppress technology readiness (Parasuraman & Colby, 2001).

Technology Acceptance Model The TAM, introduced by Davis (1986), was especially meant to explain computer usage behavior. TAM is well established as a robust, powerful and parsimonious model for predicting acceptance in the information technology domain (Venkatesh & Davis, 2000; Davis, Bagozzi, & Warshaw, 1989). It was developed to explain and predict, in particular, information technology use behavior. To analyze the adoption of ATM, the TAM of Davis (1989) is used to explain individual adoption using two constructs: perceived ease of use (PEU) and perceived usefulness (PU). Both constructs have been used to study many cases of the adoption of innovation (Lucas, Swanson, & Zmud, 2007). PU in TAM is defined as the degree to which a person believes that using a

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particular system would embrace his or her job performance (Davis, 1989). The impact of PU on system utilization is significant (Robey, 1979), whereas PEU refers to the degree to which a person believes that using a particular system would be free of effort.

Behavioral Intentions Behavioral intentions are considered to be a critical factor in explaining a customer’s behavior through which an individual’s strong intention to perform a certain behavior is likely to result in its performance (Ajzen, 1991). The concept of behavioral intentions is grounded in the theory of planned behavior introduced by Ajzen (1991), who posited that intention is determined by attitude, subjective norms, and perceived control. Ajzen and Fishbein (1980) insisted that an individual’s behavior is predictably based on his or her intentions. In this regard, numerous researchers in various settings have examined the formation of behavioral intentions to better comprehend customers’ purchasing behavior (Han, Hsu, & Sheu, 2010; Han & Ryu, 2006). While the definitions of behavioral intentions vary across the previous literature, researchers generally agree that behavioral intentions are one’s readiness/likelihood to exhibit a specific behavior (e.g., Ajzen, 1991, 2009; Han & Ryu, 2006; Oliver, 1997). Specifically, Oliver (1997) described behavioral intentions as “a stated likelihood to engage in a behavior” (p. 28). Han and Ryu (2006) defined behavioral intentions as an affirmed likelihood to perform a purchasing behavior. Ajzen (1991) asserted that one’s intention is an indicator of one’s readiness to carry out certain behaviors and that an individual’s intention can be either favorable or unfavorable. According to Zeithaml, Berry, and Parasuraman (1996), such favorable/unfavorable behavioral intentions correspond to the intention to offer positive/negative recommendations, the willingness/unwillingness to pay a premium price, and the intention to repurchase/switch. These elements have also been used to capture attitudinal loyalty, since they reflect a certain emotional commitment to a product or a brand (Yi & La, 2004).

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Service Quality The changing business environment offers challenges and opportunities to the organizations. The changing customers’ perceptions of quality pose a unique challenge. Excellence in quality has become an imperative for organizational sustainability. SQ in banking has been considered markedly important over the years; the concept has recently received even more attention. Bowen and Hedges (1993) believe that attention to SQ can contribute substantially to ameliorate the decrease in market share that banks are experiencing. Parasuraman, Zeithaml, and Berry (1985, 1988) proposed that perceived SQ can be measured by assessing the difference between customer expectations of the service encounter and the evaluation of the encounter’s outcome. Further, Gronroos (1984) suggested that SQ can be technical (what is done) or functional (how it is done). Thereafter, Gravin (1988) outlined eight attributes of quality: performance, features, conformance, reliability, durability, serviceability, aesthetics, and customer-perceived quality (Wang & Lo, 2002).

Attitude Toward Technology Knight (1994) believes that the development of a deeper understanding of customer behavior and attitude should be the most important aim of bank marketers. In this context, Fishbein and Ajzen (1975) defined “attitude” as the positive or negative feelings or affect owned by a person when he or she is engaged in a specific action. Attitudes or beliefs about various technologies have been found to be associated with technology-related behaviors. The research of Cowles (1989) and Cowles and Crosby (1990) into interactive media suggested that customers can be segmented based on how they perceive the media and that the resulting segments differ significantly in terms of acceptance of the media. Eastlick (1996) also showed that people’s attitudes and beliefs about interactive shopping influence their propensity to adopt such a mode of shopping. Pertinent literature reveals that a large number of studies have examined customers’ satisfac-

tion with ATMs. Moutinho and Brownlie (1989) evaluated the banking services, ATM services, and customers’ loyalty and identified the importance of ATMs in conditioning perceptions of the services offered by bank. Chan (1993) explored the attitude and behavior of Hong Kong college students’ toward ATMs and credit cards. Curan and Meuter (2005) examined the factors that influence customers’ attitude toward adoption of three SSTs: ATM, phone, and online banking. Chen, Chen, and Chen (2008) produced an integrated model to synthesize the essence of technology readiness (TR), TAM, and the theory of planned behavior to explain customers’ continued use of SST. Finally, Elliot and Meng (2009) explored Chinese customers’ attitude and behavior toward the use of new technology only with the help of TRI and were not able to measure the likelihood of customers to use new technology. To conclude, all the studies have taken either one or two relationships into consideration and, thus, customers’ behavioral intentions toward a particular SST have not been studied in detail. The present study fills this void by focusing on a particular SST (i.e., ATM services). Until the advent of ATMs, people were unaware and/or not directly affected by the technological revolutions happening in the banking sector. ATMs became the major revelation for customers, as it offers the facility a way to avoid long queues in front of the bank counters. It also provides the customers the flexibility of withdrawing money—anytime, anywhere (Sharma, 2009). A study conducted by the Internet and Mobile Association of India (IAMAI) found that about 53% of online users prefer ATMs as a banking channel in India, followed by Internet banking, which is preferred by only 33%. Of the 6,365 Internet users sampled, only 35% use online banking channels in India. This shows that a significant number of online users do not use banking SSTs and, hence, there is a need to understand the reasons for not using it (Geetika & Upadhyay, 2008). The study also found that Indian people are not doing financial transactions on the bank’s Internet sites mainly because of reasons such as security concerns (43%), preference for face-to-face transactions (39%), lack of knowledge about transferring online (22%),

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lack of user friendliness (10%), or lack of the facility in the current bank (2%) (Phan, 2003).

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HYPOTHESES FORMULATION Zeithaml et al. (2002) proposed that TR has a positive impact on e-shopping behavior. Among the most significant individual factors postulated as affecting customer perceptions about technology-based services is TR. Optimism and innovativeness are the positive drivers of TR, encouraging customers to use technological products and services and to hold a positive attitude toward technology, while discomfort and insecurity are the negative drivers making customers reluctant to use technology. In this context, Parasuraman and Colby (2001) found that customers’ segments with differing TR profiles vary significantly in terms of Internetrelated behavior, while Yen (2005) indicated that not all users are equally ready to embrace technology-assisted services. In India, slowly and steadily, the customers are moving toward SSTs, yet they are very concerned about the security and privacy of Internet banking (Malhotra & Singh, 2009). Hence, there are some major psychological and behavioral factors that affect the adoption of any innovation, which include security, reluctance to change, preference for personalized services, technology phobia, etc. (Srivastava, 2008). Hence, we hypothesize that H1: Indian customers are less optimistic and innovative and more uncomfortable and insecure with regard to technology adoption. In the consumer domain, TRI (Parasuraman, 2000) was developed to measure people’s general beliefs about technology. TRI has four dimensions: optimism, innovativeness, discomfort, and insecurity. Lin, Shih, and Sher (2007) conducted a study incorporating TAM and TRI, where PU and PEU (key constructs of TAM; Davis et al., 1989) were the mediators between TRI and use intentions. Thus, TRI was theorized to be a causal antecedent of both key constructs of TAM, which subsequently affect

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customers’ intentions to use e-services. The study conducted by Lin et al. (2007) integrated TRI (Parasuraman, 2000) with TAM to better explain customers’ intentions to use e-services. The technology readiness and acceptance model (TRAM) has also been proposed in exploring the acceptance of online stock trading system (Lin et al., 2007). Chen et al. (2008) integrated the constructs of TRI (Parasuraman, 2000) with TAM (Davis, 1989) to explore consumers’ longterm usage intentions toward SSTs in particular. Walczuch, Lemmink, and Streukens. (2007) measured the relationship between TR personality dimensions of TAM. The extended model of TAM (ETAM) developed by Rose and Fogarty (2006) indicates that self-efficacy, technology discomfort, and perceived risk are the determinants of PEU and PU. In fact, TR is a concept that actually helps both academicians and managers to distinguish behavior process behind the adoption of technology. In a study conducted by Natarajan, Balasubramanian, and Manickavasagam (2010), ATMs in India have been found to be the most preferred channel among the Internet and mobile banking, as one needs minimal skills to use it and less risk is involved. Therefore, we propose that H2: Indian customers who are less uncomfortable and insecure with technology adoption shall perceive it as useful and easy to use. In the technological context, a vast number of studies that followed Davis’s TAM (Benbasat & Barki, 2007) identified a relationship between PU and PEU and the adoption of a particular technology, which is defined as the attitude toward the use of technology affecting the usage intentions. Attitudes and beliefs about various technologies have been found to be associated with technology-related behavior. In this regard, Eastlick (1996) concluded that people’s attitudes and belief about interactive shopping influence their propensity to adopt such a mode of shopping. TAM established that PU and PEU of the machinery influence attitudes toward using that machinery, which in turn influence the

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individual’s intention to use technology (Adams, Nelson, & Todd, 1992; Davis, 1989; Davis et al., 1989). Rose and Forgarty (2006), while testing the intentions in TAM, predicted senior customers’ acceptance and use of banking SSTs and found that PEU and PU are the direct and indirect determinants of attitude toward intention to use SSTs. Further, Dabholkar’s (1996) study found a direct relationship between service qualities perceived for a technology-based self-service and the intention to adopt the technology. Gronroos (1984) defined service quality as a perceived judgment resulting from an evaluation process where customers compare their expectations with the service they perceive to have received. Therefore, SQ has been defined and discussed as a form of an attitude. Hence, we hypothesize that H3a: Perceived ease of use and usefulness of a technology determine the attitude toward that technology. H3b: Attitude toward a technology is also influenced by service quality. TR has been indicated as the antecedent of e-SQ. Parasuraman and Colby’s (1997) study of Internet service providers (ISPs) showed a positive correlation between TR and an ISP’s SQ. In addition, Meuter et al. (2003) suggested that technology anxiety is related to consumers’ evaluation of SSTs. Liljander, Gillberg, Gummerus, and Riel (2006) and Zeithaml et al. (2002) also suggested that customers’ readiness has a positive impact on their evaluation of e-SQ. Further, Lin and Hsieh (2006) confirmed that TR has a direct and positive influence on SST quality. Therefore, we assume that H4: Service quality of a technology significantly influences TR, which determines its adoption in future. One general finding of various studies on the adoption of technology is an evidence supporting attitude as an antecedent to behavioral intention and certain beliefs as salient antecedent to those attitudes (Adams et al., 1992; Dabholkar,

1996; Davis et al., 1989; Hebert & Benbasat, 1994). In this regard, Eagly and Chaiken (1993) proposed a composite attitude-behavior model, based on the theory of reasoned action (Fishbein & Ajzen, 1975) and the theory of planned behavior (Ajzen, 1991), which presents a more comprehensive framework of factors influencing attitude and behavior. Research has shown that attitude toward a specific SST has an impact on the intentions to use that SST (Adams et al., 1992; Davis, 1989; Davis et al., 1989). However, Curran et al. (2003) found that intention to use an SST option is driven by multiple hierarchical attitudes. Not only attitude but also SQ is linked to behavioral outcomes (Yavas, Benkenstein, & Stuhldreier, 2003). Quality has been found to be significantly correlated to repurchase intentions in the marketing literature (Zeithaml et al., 1996; Spreng, Mackenzie, & Olshavsky, 1996). Cronin and Taylor (1992), Cronin, Brady, and Hult (2000), and Dabholkar (2000) showed that SQ dimensions demonstrate positive relationships with a number of behavioral intentions either directly or through satisfaction. Gounaris, Dimitriadis, and Stathakopoulos’s (2010) three models revealed that e-SQ has a positive effect on satisfaction and influences customer behavioral intentions. Thus, SQ has a direct effect on behavioral intentions, as also revealed by Olorunniwo, Hsu, and Udo. (2006). Thus, on the basis of the preceding discussion, it is hypothesized that H5a: Behavioral intentions toward a technology are significantly predicted by attitude toward that technology. H5b: Behavioral intentions toward a technology are significantly predicted by its service quality. Attitudinal loyalty represents customers’ psychological predisposition to repurchase from the same firm/seller again and to recommend the same firm/seller (Dick & Basu, 1994). This measure of loyalty describes only intentional behavior in the next purchase occasions. Loyalty intention consists of two dimensions: likelihood of

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customer to advocate the product and repurchase intentions (Zeithaml et al., 1996). Behavioral loyalty represents the actual behavioral responses expressed over time. The measure of behavioral loyalty is operationalized on the basis of attitudinal loyalty but modified to describe actual repurchase and recommend behavior rather than intention. Behavioral loyalty is defined as a deeply held commitment to repatronize a preferred product/service consistently over time, despite situational influences and marketing efforts that might have the potential to cause switching behavior (Oliver, 1999). In their study on brand loyalty, Jacoby and Chestnut (1978) concluded that measurement of loyalty should be composite (i.e., based on both attitudinal and behavioral data). They suggested that behavioral and attitudinal data guard against each other’s deficiencies. Indeed, Anderson and Mittal (2000) asserted that intention and behavior should not be used interchangeably because of their different natures of nonlinearity. Therefore, we hypothesize that H6: Attitudinal and behavioral loyalty significantly determines behavioral intentions. Studies have indicated that customers often adopt SSTs but also commonly exhibit anxiety related to their operations (Meuter et al., 2003, 2005; Parasuraman, 2000; Yen, 2005; Zeithaml et al., 2002). De Angeli (2002) found illiteracy and lack of expertise as major deterrents to technology adoption, and the people who fell into these categories were regular users of ATMs. Walker and Johnston (2006) found that willingness to use technology-enabled service is influenced by an individual’s sense of personal capacity or capacity to engage with the specific service system that the consumer might be required to use as well as the perceived risks and relative advantages that might result from that particular use. Hence, we assume that H7: Heavy technology users are both more attitudinally and behaviorally loyal compared to light technology users.

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RESEARCH METHODS Generation of Scale Items The items under different variables covering almost all the aspects of customers’ behavioral intentions toward the ATM services of banks were generated from the relevant literature. A survey instrument was framed for the collection of requisite data. To ensure the active involvement of respondents while filling the questionnaire and to verify the internal consistency, some items were kept in negative form. Negatively worded items were reversed before data processing. The items in the questionnaire included 7 items of PU and 11 items of PEU drawn from Davis (1989) and Segars and Grover (1993). Dimensions of TRI consisted of 4 items of optimism, 3 of innovativeness, 5 of insecurity, and 3 items of discomfort extracted from Parasuraman (2000), Berger (2009), Elliot, Meng, and Hall (2008), and others. Outcome quality, operational quality (Beerli, Martin, & Quintana, 2004; Moutinho & Smith, 2000), physical quality (Grewal, Gopalkrishnan, Gotlieb, & Levy, 2007), and transactional quality (Moutinho & Smith, 2000) included 19 items relating to customers’ needs and preferences in terms of SQ of ATM services. Attitudinal loyalty comprised 8 items and behavioral loyalty consisted of 15 items extracted from Matos, Henrique, and Rosa (2009). Attitude toward ATM services comprised 8 items drawn from Moutinho and Smith (2000) and Curran and Meuter (2005).

Pretesting and Data Collection Forms To judge the appropriateness of each item and to review items for inclusion, revision, and exclusion in the final construct, pretesting was conducted. For pilot testing, 30 ATM users residing in a northern city of India were contacted on convenient basis. Initial instrument consisted of 83 items pertaining to PEU, PU, attitude, SQ, and attitudinal and behavioral loyalty. After the pilot study, it was found that some ATM users resist their technological interactions, despite its various benefits. Thus, it was felt that customers’ TR should be taken into account in order to accurately predict their perceptions and behavior

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toward ATM services. Therefore, dimensions of TR (i.e., optimism, innovativeness, discomfort, and insecurity; Parasuraman, 2000) were added to the final instrument. A 5-point Likert scale ranging from 1 (strongly agree) to 5 (strongly disagree) was used. During pilot testing, it was observed that most of the bank customers either have Bank1 or Bank2 (names not disclosed to maintain confidentiality) as their prime bank for using ATM services, and thus, it was decided that during the final survey the data would be collected only from these two banks. For measuring the behavioral intentions of customers toward ATM services, the instrument was tested through first-hand information gathered from bank customers. A pilot survey of 30 respondents selected from a city of northern India in 2010 helped in working out the mean and variance in the population so as to determine an appropriate sample size (Malhotra, 2007, pp. 400–402), which was finally set at 268. A list of households in that city was obtained from the municipal corporation; in 2010, the total number of households was 4145. The 268 respondents were contacted using systematic sampling technique. The first respondent was selected purely on random basis and, thereafter, every 15th house was contacted for the collection of required data. Of 268 households, 250 completely filled-in questionnaires were obtained. While collecting the data, the household having neither Bank1 nor Bank2 as its prime bank was not considered as part of the sample. Further, households not having complete information regarding ATM services were also not included in the data collection process.

Validity and Reliability Validity of the scale was established through convergent and discriminant validity. High factor loadings (i.e., above .50 or ideally .70 or higher) indicate level of convergence. Convergent validity is established in the present study as the majority of loadings are above .50 (Table 1). In confirmatory factor analysis (CFA), the average percentage of variance extracted (VE) is a summary indicator of convergence. If AVE is above .50, convergent validity gets established, and in our case, AVE for almost all the constructs

is above .70 (Table 2). Discriminant validity was assessed by applying χ 2 difference test to each of the possible pairs of the study measurement scales. The χ 2 difference test indicates that the hypothesized unconstrained model is superior to the constrained model. For all the pairs of constructs, χ 2 values were significant at .05 level of significance (χ 2 > 3.84, df = 1), thereby indicating that models are different (Ahire, Golhar, & Walker, 1996). As a result, it can be concluded that each of the latent variable in the final model exhibits discriminant validity. Cronbach’s α indicator was used to assess the initial reliability of the scales, wherein a value above .60 is generally considered as acceptable criterion for demonstrating internal consistency (Gerrard & Cunningham, 2003; Ngobo, 2004). Its value was above .70 in all the cases, thus signifying the reliability of our data (Table 2).

ANALYSIS AND DISCUSSION Analysis of the descriptive statistics of dimensions for demographic profile of ATM users reveals that there is not much significant difference between the mean scores of the respondents toward the dimensions under study. The results of t test and analysis of variance (ANOVA) signify the magnitude of differences among ATM users belonging to different sexes, banks, ages, incomes, professions, and education levels. Genderwise analysis depicts significant difference among males and females only with respect to TAM. On the basis of age, ATM users differ only for TRI dimension. Further, sampled bank customers belonging to different incomes and professions possess similar views with respect to all the dimensions under consideration. However, with respect to education level of ATM users, they differ significantly with regard to TAM, TRI, and overall SQ. Finally, bankwise analysis indicates no statistical difference between ATM users of public and private sector banks. Overall, it can be safely concluded that bank customers of both public and private banks have formed similar opinions with respect to TAM, TRI, overall SQ, attitude, and behavioral intentions (Table 3). A three-step procedure of data analysis was adopted wherein exploratory factor analysis

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TABLE 1. Descriptive Statistics

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Dimensions Perceived usefulness Useful for obtaining banking information Useful for searching and availing and availing new products and services Useful for enhances effectiveness Perceived ease of use User friendly Easy to use Clear cut instructions Easy options Easy withdrawal Easy checking of balance Requires minimum efforts easy to become skillful Easy to operate machines Optimism Preference for advanced technology ATM brings efficiency ATM facilitates mobility Innovativeness Being innovator Updated knowledge Updated technology Discomfort Precaution for replacing people with technology Risky new technology Easy spying through technology Insecurity Unsafe to give PIN Unsafe to do business online Written confirmation Risky to carry ATM card Risky to make payments Easy tracing of PIN Attitude Frequent use Satisfactory ATM operations Assistance during contingencies Preference for ATM Encouragement to others Saves time Outcome Quality Sufficient number of ATM Quick delivery of services Convenient location Operational Quality 24-Hour service Quality currency notes Less time consuming Minimum queuing time Equivalent to personalized services

Mean

SD

FL

SRW

CR

4.08 3.44

.961 1.32

.685 .922

.478 .986

7.892 10.221

3.53

1.28

.896

.812

...

4.42 4.44 4.22 4.28 4.39 4.28 4.24

.697 .670 .858 .838 .790 .795 .818

.806 .661 .803 .749 .831 .596 .637

.733 .718 .833 .788 .719 .736 .668

9.583 9.432 10.502 10.103 9.442 9.612 8.926

4.18

.810

.782

.715

9.402

4.24 4.07 4.13

.788 906 .803

.829 .824 .714

.683 .833 .853

7.278 7.941 8.000

3.40 3.39 3.74

1.18 1.24 1.02

.857 .771 .718

.395 .448 .510

5.094 5.594 ...

3.40

1.07

.830

.778

10.613

3.48 3.27

1.13 1.24

.890 .784

.873 .702

10.587 ...

3.31 3.15 3.38 3.47 3.47 3.36

1.218 1.224 1.204 1.22 1.22 1.27

.794 .892 .739 .866 .864 .759

.759 .812 .846 .456 .499 .511

7.685 7.905 8.013 5.682 6.044 ...

4.02 4.108 4.16 4.36 4.24 4.33

.90 .801 .838 .77 .856 .848

.628 .718 .763 .754 .633 .504

.563 .624 .722 .631 .608 .621

7.060 6.969 7.132 7.678 7.085 ...

3.40 3.80 3.67

1.27 .988 1.11

.855 .688 .845

.761 .723 .874

11.791 11.311 ...

3.92 4.17 4.24 3.84 3.95

1.03 .85 .83 .98 .95

.636 .802 .794 .567 .555

.687 .725 .705 .532 .557

7.449 7.652 7.552 6.346 ...

α

Composite reliability

.80

.80

.91

.94

.79

.78

.83

.90

.77

.86

.82

.79

.77

.79

(Continued)

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TABLE 1. Descriptive Statistics (Continued)

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Dimensions Physical Quality Clean environment near ATM booth Well-designed booth layout Personal security Transactional Quality Accurate recording Accurate performance Written guarantee Numerous options Behavioral Loyalty Recommendation to family Recommendation to friends Assurance to others Promotion through word of mouth Repeat visits Attitudinal Loyalty Trustworthy ATM services Better ATM services Superior ATM services Patronized ATM services Customized ATM services Better performances Sense of obligation Staying intentions Safeguarding bank’s image Quick referrals

Mean

SD

FL

SRW

CR

3.56 3.48 3.47

1.05 1.03 1.09

.822 .881 .801

.795 .882 .775

12.556 13.189 ...

4.06 3.95 4.02 3.92

.820 .783 .799 .792

.820 .783 .799 .792

.753 .789 .780 .750

11.182 11.653 11.182 ...

4.08 4.11 3.96 4.03 3.96

.905 .946 .921 .832 3.33

.621 .651 .514 .598 .524

.790 .792 .712 .716 .614

9.414 9.424 8.802 8.840 ...

4.04 3.99 3.92 4.06 4.02 4.09 3.95 4.068 4.024 3.65

.841 .878 .910 .886 .918 .811 .932 .816 .796 1.08

.701 .725 .750 .626 .815 .831 .770 .676 .631 .922

.683 .671 .689 .826 .808 .826 .747 .650 .558 .210

7.362 7.295 7.393 8.030 7.955 8.028 7.688 7.175 6.580 3.040

α

Composite reliability

.85

.84

.86

.86

.83

.83

.92

.93

Note. SD : standard deviation; FL: factor loading; SRW: standardized regression weight; CR: critical ratio.

TABLE 2. Reliability and Validity of Latent Constructs

Constructs PU PEU Inn/Opt Dis/insec Transq Oprq Outq Phyq Attloy Behloy At

AVE

Composite reliability

Cronbach’s α

.62 .51 .42 .68 .59 .40 .61 .67 .48 .46 .38

.80 .94 .78 .90 .86 .79 .79 .84 .93 .83 .86

.80 .91 .79 .83 .86 .77 .82 .85 .92 .83 .77

Note. AVE: average variance explained; PU: perceived usefulness, PEU: perceived ease of use; Inn: innovativeness; Opt: optimism; Dis: discomfort; insec: insecurity; Transq: transactional quality; Oprq: operational quality; Outq: outcome quality; Phyq: physical quality; Attloy: attitudinal loyalty; Behloy: behavioral loyalty; At: attitude.

Male Female p value < 25 26–35 36–45 > 45 p value ≤ 50,000 50,000–1 lac 1 lac–2 lac > 2 lac p value Metric Graduate Postgraduate Others p value Business Service Profession Others p value Private Public p value

Categories

3.94 4.10

3.94 4.05 4.06 3.95

3.77 4.07 3.96 4.12

4.01 3.95 4.23 3.79

4.04 4.06 3.95 3.53

3.95 4.04

Mean

.589

.511

.011

.059

.163

.026

TAM

.61 .61

.62 .57 .41 .73

.55 .69 .64 .47

.57 .61 .56 .80

.69 .56 .54 .79

.64 .53

SD

2.71 2.67

2.58 2.74 2.49 2.74

2.28 2.83 2.70 2.47

2.71 2.72 2.85 2.49

2.84 2.81 2.32 2.19

2.69 2.69

Mean

.557

.623

.013

.20

.000

.622

TRI

.59 .87

.96 .77 .82 .98

.82 .93 .79 .81

.86 .99 .79 .85

.90 .88 .72 .64

.89 .85

SD

3.74 3.85

3.73 3.77 3.70 3.86

3.65 3.90 3.71 3.78

3.84 3.79 3.83 3.66

3.89 3.74 3.77 3.64

3.75 3.86

Mean

.566

.383

.039

.30

.148

.138

OSQ

.88 .61

.61 .52 .39 .65

.44 .60 .55 .58

.60 .60 .46 .58

.63 .51 .54 .51

.59 .51

SD

Note. TAM: technology acceptance measures; TRI: Technology Readiness Index; OSQ: overall service quality; BI: behavioral intentions.

Bank

Profession

Education

Income

Age, yr

Sex

Demographic variable

4.19 4.22

4.21 4.20 4.13 4.23

3.99 4.29 4.17 4.26

4.29 4.09 4.29 4.13

4.29 4.23 4.03 4.01

4.20 4.22

Mean

.913

.310

.068

.11

.05

.718

Attitude

TABLE 3. Descriptive Statistics of Various Dimensions of Behavioral Intentions

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.54 .64

.52 .52 .48 .73

.70 .64 .47 .43

.55 .59 .62 .58

.67 .46 .67 .51

.61 .54

SD

3.94 4.04

3.85 3.97 4.09 4.04

3.81 4.06 3.91 4.23

4.046 3.94 3.97 3.93

4.05 3.99 3.83 3.88

3.95 4.04

Mean

.399

.795

.056

.76

.124

.129

BI

.55 .64

.66 .57 .52 .67

.57 .68 .53 .36

.65 .69 .49 .58

.69 .55 .53 .58

.63 .53

SD

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(EFA) was performed initially, followed by CFA and estimation of structural model through structural equation modeling. EFA, conducted to determine the underlying dimensions of behavioral intentions of customers’ toward ATM services, resulted in 17 factors (Table 1). These factors have been labeled as effective information generation, perceived ATM amenities, attitudinal loyalty, behavioral loyalty, PEU, risk, physical quality, outcome quality, user-friendly ATM services, operational quality, insecurity, discomfort, innovativeness, optimism, accessibility, attitude, and accuracy. CFA was performed on four major constructs of the model (TAM, TRI, SQ, and behavioral intentions). The pertinent literature supports that TRI is best represented by optimism, innovativeness, discomfort, and insecurity; however, in the present study, optimism and innovativeness did not load significantly and therefore were dropped. This result is in contradiction to the study conducted by Godoe and Johanses (2012) in Norway, where optimism and innovativeness were the significant predictors of technology adoption. The reason may be that cultural differences exist between developed countries like Norway and emerging countries like India. CFA was performed on discomfort and insecurity to determine TR, and results reveal that insecurity (β = .58) and discomfort (β = .71) prevail among Indian customers with regard to new technology. Hence, it can be safely concluded that bank customers in emerging markets are risk averse and, thus, have a strong belief toward safe transactions. A study conducted by Dixit and Datta (2010), with reference to Indian context, found that customers are ready to adopt online banking if the bank provides necessary guidelines regarding security and privacy aspects, because there are many factors (trust, innovativeness, and awareness) that affect the acceptance of online banking in India. Further, the results indicate that TAM is best predicted by PU and PEU. However, PU has higher significant standardized loading (β = .826) than PEU (β = .402). CFA was also run on SQ, which is determined through outcome quality, operational quality, physical quality, and transactional quality. The findings reveal that SQ is more significantly determined with the help of outcome

quality (β = .47) and physical quality (β = .396), followed by transactional quality (.359). However, the contribution of operational quality toward assessing SQ in the banking sector is the least (β = .239). The study findings pertaining to behavioral intentions reveal that these intentions are significantly determined through attitudinal loyalty and behavioral loyalty. After performing CFA, the structural model was estimated. While estimating structural model, TRI seems to be best predicted by discomfort (β = .248) and insecurity (β = .451; Figure 1). However, innovativeness and optimism appeared to be insignificant (during CFA) in predicting TR, thus partially accepting H1. A similar finding is revealed by the study conducted by Elliott, Meng, and Hall (2008), whereby Chinese consumers exhibit higher levels of discomfort and insecurity and lower levels of optimism and innovativeness with regard to using new technologies. The model further explains that TRI influences PEU significantly and positively (β = .725), but TRI negatively influences PU (β = −.315). Feelings of insecurity related to technology are associated with ambiguity and low use (Parasuraman & Colby, 2001; Tsikriktsis, 2004). Discomfort, on the other hand, is not expected to have a negative impact on PU, but a system that is not manageable is more likely to be a non–user-friendly system. Therefore, both TRI components negatively influence PU, which is also confirmed by Godoe (2012); hence, H2 is partially accepted. Only PU of TAM has a direct effect on attitude toward ATM (β = .76), whereas PEU does not significantly affect the attitude of customers toward SST, which implies partial acceptance of H3a. In contrast, findings of the study by Rose and Forgarty (2006) reveal that both PU and PEU have a significant influence on attitude. The results of structural model further highlight that SQ, which is predicted by outcome quality, operational quality, transactional quality, and physical quality, significantly influences attitude toward ATM (β = .345), thus accepting H3b. This finding is supported by Dabholkar (1996) and Gronroos (1984). Further, there appears to be a significant correlation between TRI and service quality (β = .64), also supported by

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FIGURE 1. Structural Model of Behavioral Intentions. Note. disc: discomfort; insec: insecurity; TRI: technology readiness; peu: perceived ease of use; pu: perceived usefulness; attitud: attitude; atlolty: attitudinal loyalty; blolty: behavioral loyalty; osq: overall service quality; behint: behavioral intentions.

osq

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disc

insec

attitud

.55

TRI .45

.54 -.10

.72

.25

.31

.34

peu

-.31

.87

.65

pu Zeithaml et al.’s (2002) prepositions, thus accepting H4. Liljander et al. (2006) also suggested that customers’ readiness has a positive impact on their evaluation of e-SQ. Further, Lin and Hsieh (2006) confirmed that TRI has a direct and positive influence on SST quality. While predicting behavioral intentions, the model shows that both attitudes toward ATM and SQ significantly predict behavioral intentions (β = .772 and .307, respectively), thus accepting H5a and H5b. Adams et al. (1992), Dabholkar (1996), Davis et al. (1989), and Hebert and Benbasat (1994) obtained a similar finding. Curran et al. (2003) also found that intention to use SST options is driven by multiple hierarchical attitudes. SQ has a direct effect on behavioral intentions, as also revealed by Olorunniwo et al. (2006). TABLE 4. Results of Absolute and Incremental Goodness-of-Fit Indices

.77

atlolty

1.00

usage

behint .91

blolty

The study findings pertaining to behavioral intention reveal that it is significantly determined through attitudinal loyalty (.865) and behavioral loyalty (.91), hence accepting H6. The final model has been found to have a good fit, as its χ 2 statistic was nonsignificant and below the recommended level (i.e., 5.0). Other indices were also considered and found to be appropriate. The various absolute fit indices such as GFI (.983), AGFI (.952), RMSR (.027), and RMSEA (.043) also revealed a good model fit. The two incremental fit indices, CFI (.989) and TLI (.977), were also found to be acceptable, as their values were above .90 (Table 4). Multivariate analysis of variance (MANOVA) was applied to test the group difference between heavy and light ATM users, and result reveals that heavy ATM users are more attitudinally as well as behaviorally loyal compared with light ATM users, thus supporting H7 (Tables 5 and 6).

TABLE 5. Descriptive Statistics of MANOVA

χ2/df (p)

1.452 (.248)

RMR RMSEA GFI AGFI NFI CFI TLI

.027 .043 .983 .952 .971 .989 .977

Dependent variables

Categorical variables

Mean

SD

Attitudinal loyalty

Heavy users Light users Heavy users Light users

4.08 3.50 4.11 3.46

.525 .899 .663 .904

Behavioral loyalty

ATTLOY BEHLOY ATTLOY BEHLOY ATTLOY BEHLOY ATTLOY BEHLOY ATTLOY BEHLOY ATTLOY BEHLOY

Dependent variable

Note. BEHLOY: behavioral loyalty; ATTLOY: attitudinal loyalty. a 2 R = .085 (adjusted R 2 = .080). b 2 R = .072 (adjusted R 2 = .068).

Corrected total

Total

Error

AT1

Intercept

Corrected model

Source

df 1 1 1 1 1 1 210 210 212 212 211 211

Type III sum of squares 6.302a 7.712b 1043.425 1038.661 6.302 7.712 68.068 99.498 3524.618 3586.160 74.370 107.210

6.302 7.712 1043.425 1038.661 6.302 7.712 .324 .474

Mean square

TABLE 6. Tests of Between-Subjects Effects Through MANOVA

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19.442 16.277 3219.109 2192.193 19.442 16.277

F

.000 .000 .000 .000 .000 .000

Sig.

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CONCLUSION The present study proposed a model to synthesize the essence of two models (TR and TAM) explaining customers’ intentions to use ATM. Owing to the growth of SSTs in the banking sector, it is essential for researchers to understand customer use and perceptions of SSTs. It is found that consumers are driven to adopt an innovation primarily because of the usefulness of the technology for them and due to the ease to use that technology. PU and PEU are thus the two measures that significantly influence customers’ attitude and intentions to use SSTs. Also, PEU is a significant determinant of PU. Consumers are likely to be more satisfied with SSTs if they believe that using the system will increase their performance and productivity (Wang, Hsieh, Butler, & Hsu, 2008). However, some consumers are restricted by their competence from effectively interacting with SSTs, and thus, technology is experienced positively by some and negatively by others (Meuter et al., 2000; Mick & Fournier, 1998). Customers feel uncomfortable when confronted with an SST, resulting in frustration with technology-based systems. In addition, Lin and Hsieh (2006) indicated that SST providers should reduce TR inhibitors in order to raise TR of customers as a whole. The results of Walczuch et al. (2007) also found that discomfort and insecurity provide only a marginally negative effect on PU and PEU. The present study confirms the findings of Bobbitt and Dabholkar (2001) that consumer attitudes have a strong and direct effect on intentions to use technology-based selfservice. Also, TR and SQ are correlated to each other, which support the findings of the study conducted by Parasuraman and Colby (1997). Thus, it can be concluded that people tend to use SSTs if they have a need for it, if they are convenient, if they perceive it to be easy to use, if they feel it safe, and if they have a positive attitude toward the technology.

MANAGERIAL IMPLICATIONS Despite extensive use of ATMs, the absence of direct interaction with bank staff has increased

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customers’ apprehensions about the perceived risk. To reduce the customers’ concern about perceived risk because of security and privacy concerns, the bank should improve the quality of interaction with the customers to alleviate these apprehensions. To improve the service quality, ATM services should be able to provide enhanced interactivity, diversified offerings, and facilitate customers to participate in improving the service encounter with ATM. Banks should pay attention toward the training of their employees who have direct contact with customers so as to improve customers’ overall satisfaction. To restore customers’ faith in ATMs, bank managers need to introduce programs to address technological issues. ATM networks should be properly distributed according to the demographic structure of the region so that it is easily accessible to people in every corner of the region. More services, like cash depositing, should be introduced by the banks. Closed-circuit televisions should be installed in the ATMs so that hacking of customer IDs can be monitored. Enhanced salience of ATM to customers’ needs, greater compatibility of ATM to customers’ banking norms and lifestyle, less complex and easy-to-use systems that do not require a lot of mental and physical effort to accomplish transactions (e.g., easy to read comprehensive information or instructions on the system, prompt processing of transactions, interactivity, etc.), and opportunity for adopters to experiment with the system should be ensured by the banks.

LIMITATIONS AND FUTURE RESEARCH The present study was confined to two banks and was limited to the branches of these banks operating in only one city. Moreover, there are various self-service facilities that are provided by banks such as online banking and telephone banking, but this study was confined to only one SST (i.e., ATMs). Personality traits of customers also affect use behavior, which have not been studied. Culture has a great impact on technology adoption, which is not taken into consideration. The present study did not explore the effects of the demographic characteristics or individual

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differences of customers, such as age, education level, previous experience, etc., on technology adoption. Only two models (i.e., TAM and TRI) have been studied; there are many more that were not covered in the present study.

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