What are costs that make customers reluctant to switch to another product?

[Acknowledgement: This research was partially supported by The Hong Kong Polytechnic University Research Fund.]

ABSTRACT -

Reduced regulation, increased price competition, and diminished consumer loyalty have propelled customer retention and customer relationship management (CRM) to the forefront of marketing concerns. The role of switching costs in customer retention has been posited, but has not been subjected to rigorous empirical testing. Therefore, our main focus is on the factors that influence whether consumers will switch to alternative technologies or stay with an incumbent technology. Based on consumer survey data, we find empirical support for the link between technology compatibility strategies and consumer’s expertise in technology and commitment to a particular technology. Specifically, switching costs are found to be positively associated with technology commitment. Further, the lack of expertise on the part of consumers tends to increase the likelihood that they will rely on an existing technology, rather than switch to a new one.

In the fast-changing and competitive technology market, every firm tries to provide the most advanced version of whatever product they offer. In the computer software market in particular, companies regularly update and upgrade their products in order to encourage a commitment to the technologyBthe repeated purchase or continuous use of a particular type of technologyBon the part of current users, as well as to entice new users. Alternatively, the complexity that consumers face when making decisions about which technology to use stems to a large degree from the rapid pace at which technology has advanced and the variety of technology alternatives (Bourgeois and Eisenhardt, 1988; Ryuter et al., 2001; Tushman and Anderson, 1986).

Among the many factors that encourage commitment to a particular technology, one that has received scholarly attention in other contexts is switching costs. 'Switching costs’ are the psychological, physical, and economic costs that consumers face in switching between technologies (Jackson, 1985). As competition intensifies and the costs of attracting new customers increase, companies are increasingly focusing their strategic efforts on retaining customers (Jones et al., 2000). Obviously, a key component in any customer retention program is satisfaction (e.g., Cronin and Taylor, 1992). However, satisfaction need not be the only strategy (Fornell, 1992). Barriers to customer defection, such as the development of strong interpersonal relationships or the imposition of switching costs, represent additional retention strategies. Despite their potential importance in the retention process, the role of switching costs has received relatively little attention in the field of marketing (Anderson, 1994; Jones et al., 2000, 2002).

The encouraging a commitment to the incumbent technology plays a role in customer retention has been posited (Ryuter et al., 2001), but has not been subjected to rigorous empirical testing. Therefore, our main focus is on the factors that influence whether consumers will switch to alternative technologies or stay with an incumbent technology. Specifically, we attempt to demonstrate the importance of switching costs in the success that corporate technology advancement strategies (such as the promotion of a compatible complementary technology and the pace of technological change) have had in securing commitment to a particular type of technology. We argue, however, that the success of these seemingly disparate strategies actually depends to a significant degree on the same underlying factor, i.e., switching costs. Further, uncertainty caused by the consumer’s lack of expertise also can play a major role in the decision to commitment to a technology. In the process, we hope to provide an integrative framework for understanding at least some of the mechanisms by which technology advancement strategies and the consumer’s technology expertise of the consumer affect technology commitment decisions.

CONCEPTUAL MODEL AND RESEARCH HYPOTHESES

Compatibility of Complementary Technologies

Many of the products are used not in isolation but integrated with one or more complementary products. The value of products and services depends on the number or variety of compatible complementary goods or services (Katz and Shapiro, 1985). For instance, CD players are used with CDs, video game consoles with video games, and computer operating systems with software programs. All of these have one thing in common, namely coexistence: they need each other. Consumers are more likely to purchase items that are either compatible with their existing equipment or likely to be compatible with future products in the same category. When consumers purchase products in the form of components that must be put together, technological compatibility between components becomes a factor in the evaluation of the end product (Kotabe et al., 1996).

Alternatively, compatibility o technology is associated with the cost to the consumer of switching technologies. Complementary goods provide system benefits: the added value to users of the full system. The incremental benefits provided by the whole can be greater than the sum of the benefits of the individual components. System benefits usually increase switching costs (Jackson, 1985; Shapiro and Varian, 1999). Therefore, system benefits and the increased cost of switching between whole systems are effective in keeping consumers committed to the technologies they are currently using. To the extent that the existing-version adopter continues to derive a satisfactory consumption value from the entire system and to the extent that the consumer’s systemwide investment (in complementary products, interfaces, and learning) is neither transferable to the new version nor recoverable from the disposal of the existing version, the consumer will be even more reluctant to switch (Dhebar, 1996). Therefore,

H1: The existence of compatible complementary products will be positively associated with the costs of switching from an incumbent technology.

Pace of Technological Change

High-technology environments are of particular interest to practitioners and scholars alike because their higher rates of change result in greater technological heterogeneity, and because of the implications of increasing uncertainty (Glazer, 1991). In light of the fast-changing and competitive high-technology markets, a particular type of technology would become obsolete very quickly, with implications for marketing strategies and for the evaluation of vendor performance across time or using criteria sensitive to changes in technology (Smith et al., 1999).

The pace of technological change is defined as the rate at which the focal technology and its features are changing (Weiss and Heide, 1993). In recent times, the time interval between successive generations of high technology products has been very short. An extreme example of this is the computer software industry, where firms introduce a series of upgrades at a rapid pace. A prominent case in this sector is Microsoft Corporation which introduces upgrades for its operating system Windows approximately once every two years.

As suggested by Sutton, Eisenhardt, and Jucker (1986), rapid changes in technology make it difficult for buyers to evaluate acquired information in terms of the significance of new technology offerings. This, in turn, gives consumers an incentive to stay with the incumbent technology, even after having collected information about new ones. This prediction is also supported by studies showing that rapid change represents uncertainty because of the time sensitivity of information (Bourgeois and Eisenhardt, 1988). Under such conditions, information gathered at a particular point in time may not remain relevant for long: thus making a decision to buy a new and relatively unknown technology introduces the risk of obsolescence (Eisenhardt, 1989). Consumers are reluctant to switch not because they do not value the improvement, but because early in the life of the existing version, the benefits from switching are not commensurate with the costs of switching (Dhebar, 1996). Hence,

H2: The more rapid consumers perceive the pace of technological change to be, the higher their switching costs.

Expertise in Technology

Expertise in a product (or a technology) allows consumers to more rapidly and accurately evaluate options and learn new product-related information (Alba and Hutchinson, 1987).Consumers gain expertise when they increase their product-related experiences (Burnham, et al., 2003; Park, et al., 1994). As compared to novices, experts are better able to recognize the complexities in a problem and to process information analytically. In a decision to purchase, experts recognize important product attributes, operate from better-established decision criteria, and thus are more capable of making decisions independently. Consumers with more prior knowledge will analyze attributes of quality, beliefs, and judgments about products more quickly than those with less prior knowledge when quality cues are not unexpected (Heiman et al., 2001; Sujan, 1985).

Prior research has examined search efficiency as one of the predictors of consumer search levels (e.g., Brucks, 1985; Ratchford and Srinivasan, 1993). Two important factors influencing search efficiency include a consumer’s knowledge and/or experience about the market and exposure to relevant information during the search process (Ratchford and Srinivasan, 1993). A greater degree of market knowledge and exposure to relevant information will enable the consumer to examine only the appropriate relevant sources of search (and ignore the irrelevant sources), thereby enhancing the efficiency of the search. Search efficiency also makes it easier for the consumer to acquire and process new information (Brucks, 1985).

Therefore, experts will need to expend less effort in learning new technologies, enabling them to adapt new ones more efficiently. As they need less effort to search for information and to assess alternatives, the costs of switching will decline (Kerin et al., 1992). Thus compared to novices, expert consumers find it much easier to search for information, evaluate it, and learn an alternative technology. With this regard, expert consumers will be less reluctant than novices to adopt an alternative technology.

H3: Technology expertise will be associated with lower switching costs.

Consequence of Switching Costs: Behavioral Intentions

Switching costs refer to costs expressed as the time, efforts, and financial risk involved in switching from a particular type of technology. Pre-switching search and evaluation costs represent consumer perceptions of the time and effort involved in seeking out information about available alternatives and in evaluating their viability prior to switching (Zeithaml, 1981). Learning also occurs after switching, as consumers adjust to a new alternative. Consumer perceptions of the time and effort needed to acquire and adapt to these new procedures and routines are referred to as post-switching behavioral and cognitive costs. All else being equal, the higher perceived costs of switching should reduce the likelihood that consumers will switch service providers (Anderson, 1994; Jones et al., 2002). Switching costs may be a significant impediment to the adoption of a new technology, acting as a barrier to new entrants by making consumers favor incumbent technologies (Porter, 1980).

High-technology markets are characterized by a high level of uncertainty. Rapidly changing technologies and the absence of relevant information are the main sources of this uncertainty (Heide and Weiss, 1995). This means that the costs and risks involved in switching from a technology will influence the choice behavior of consumers. Therefore, switching costs create dependence and inertia; new technology keeps getting more costly for new consumers, at least in terms of the time required to master it. Consumers’ anticipation of high switching costs gives rise to their interests in maintaining a continuous relationship and commitment to incumbent technologies (Dwyer et al., 1987).

Consumers who develop nontransferable product-specific skills may be unwilling to learn how to use a new product (Alba and Hutchinson, 1987). The effect grows with time, and consumers are forced to commit to incumbent technologies as the costs of switching continue to increase (Kotabe et al., 1996). Further, commitment has been conceptualized in terms of a temporal dimension, focusing on the fact that commitment becomes meaningful only when it develops consistency over time (Moorman et al., 1992). As a result of continuity, consumer turnover may be reduced and a relationship can be maintained (Ganesan, 1995).

H4: The costs of switching technologies will encourage commitment to the incumbent technology.

RESEARCH DESIGN

Product

For this study, we chose a personal computer operating system (e.g., MS Windows) as a key product. This is mostly used by individuals and small business. First of all, the personal computer operating system is a well-known and crucial product for computer users. Second, in the network market, a personal computer operating system requires compatibility with other applications software. Third, an operating system can be upgraded, and indeed companies regularly offer upgraded versions. Finally, changing from one operating system to another imposes switching costs.

Questionnaire Development and Data Collection

Measures for the variables were either developed specifically for this study or adapted from prior ones. In cases in which the measure was developed for this study, the domain of the relevant construct was first specified and the items subsequently developed on the basis of the conceptual definition. The items were then modified on the basis of field interviews, reviews of literature, and discussions with industry observers. The measures were subsequently pre-tested and modified again, if necessary. In cases in which the scale was adapted from prior studies, the wording of the original items was changed so as to make sense to respondents in the present context: as then the material was then used in a pretest. In addition, we refined the measure scales throughout the purification procedures. Table 1 shows the items used in this study.

TABLE 1

ITEMS AND INTERNAL CONSISTENCY

The finalized questionnaire was mailed to 730 computer users in metropolitan areas in Korea. Each individual respondent was contacted in advance by phone to request his/her cooperation; in order to increase the response rate, follow-up calls were made and the participants were reassured that all responses would be kept confidential and that only the aggregate results would be presented. Of the 730 questionnaires distributed, 476 were finally returned with usable data, providing for a 65.2 percent response rate. Among the 476 respondents, 413 (86.8%) were MS Windows users, 44 (9.2%) were Mac OS users, and the remaining 19 (4.0%) were LINUX users. Of the 476 respondents, 356 (74.8%) indicated that they had used a computer for at least three years. All items were answered through a seven-point Likert-type scale ranging from "Strongly Disagree" to "Strongly Agree."

Nonresponse bias was examined by comparing early with late respondents as suggested by Armstrong and Overton (1977). We defined the early-respondent group as the first 60% of the total respondents that returned the questionnaire earlier than the remaining 40% (late-respondent group). We then compared these two groups based on age, sex, and years of computer used. We performed another comparison between the first 75% early respondents and the remaining 25% late respondents on the same variables. No significant differences between the two groups on these variables were found, suggesting that nonresponse bias was not a major problem.

RESULTS

Measurement Model Results

Consistent with the two-step approach advocated by Anderson and Gerbing (1988), we estimated a measurement model prior to examining the structural model relationships. We used LISREL 8.14 (Joreskog and Sorbom, 1998) with covariances as the input to estimate the model. The goodness-of-fit index (GFI), the adjusted goodness-of-fit index (AGFI), and the comparative fit index (CFI) values were .92, .89, and .91, respectively, which means that the measurement model fits the data well (Kelley et al., 1996). The parsimony normed fit index (PNFI) value was .73 (minimum acceptance is .60) and the root mean square error of approximation (RMSEA) was .058. Taken collectively, the indices seem to show a reasonable fit, even though the chi-square index is significant (c2=287.38; p<.01).

FIGURE 1

STRUCTURAL EQUATION MODEL RESULTS

Composite reliability and coefficient alpha provide evidence of internal consistency. Composite reliability is a LISREL-generated estimate of internal consistency analogous to coefficient alpha (Fornell and Larcker, 1981). As Table 1 shows, these two estimates ranged from .60 to .86. To investigate the convergent validity of the scales, we performed a confirmatory factor analysis using Maximum Likelihood (ML) estimation in LISREL 8.14. We have found that all factor loadings from latent constructs to their corresponding measurement items are statistically significant (i.e., t>2.0; minimum t-value of the factor loadings is 8.54).

Structural Equation Model Results

The first hypothesis posits that compatibility with complementary technology is associated with the costs of switching from an incumbent technology (H1). According to Figure 1, the positive relationship between compatibility with complementary technology and switching costs proves to be robust based on the corresponding coefficients (b=.35; p<.01). The existence of a variety of compatible complementary products that use existing technology will encourage consumers to maintain their preference for the incumbent technology due to the costs of switching.

H2 is concerned with the impact of the pace of technological change on technology commitment decisions of consumers via switching costs. Specifically, we posit that the pace of technological change will be associated with the costs of switching. Even though the paths show a positive impact, the relationship is not significant (b=.03; n.s.).

H3 posits that a consumer’s expertise in technology has a negative impact on switching costs. The hypothesis is supported based on corresponding coefficients (b=-.20; p<.01): if consumers are novices on technology, they tend to rely more on existing technology because of the costs of switching. Last, we have attempted to establish the association between switching costs and its consequence, i.e., commitment to a particular technology. The hypothesis posits that switching costs encourage consumers to stay committed to a particular type of technology (H4). As we have noted in Figure 1, switching costs secure technology commitments (b=.34; p<.01).

DISCUSSIONS AND CONCLUSION

When consumers have built up large technology-specific switching costs, they tend to commit to incumbent technologies and put less effort into their searches and decision processes. The knowledge that the adoption of new technology is likely to involve nontrivial levels of switching costs creates a disincentive for consumers to search outside the established portfolio and may result in constrained search processes (Jackson, 1985; Shapiro and Varian, 1999).

As switching costs act as an entry barrier against new entrants to the market (Porter, 1980), and these invisible barriers are voluntarily established by consumers, incumbent technologies can easily maintain (or increase) their market shares. As a consequence of their constrained searches, consumers with strong relationships with certain technologies may perceive less change to have taken place in the market than has actually occurred; this in turn lowers their incentives to engage in market searches. The presence of high switching costs therefore tends to buffer consumers from information about competing technologies and to show continuous commitment to incumbent technologies.

Unfortunately for new entrants into high-technology markets, the results of this study support the conclusion that where a dominant technology emerges, switching costs may make its position unassailable unless there is a fundamental shift in the technology paradigm. The costs to the consumer of switching from one standard to another can be considerable, not only in terms of having to purchase of new software, but also in terms of the difficulty of properly exploiting the new package. Thus, for example, a consumer who switches to a technically superior but unpopular spreadsheet that has a different command set and macro programming language will find it harder, and therefore more costly, to get complementary products. This is because producers of complementary products are likely to concentrate on the more lucrative standard market.

From a managerial perspective, the results of this study raise some issues that have implications for marketing practice. We want to emphasize, however, that these implications should be viewed with some caution because of the descriptive nature of the study and the fact that the results, at this point in time, are based only on a single study. Under this general caveat, the results have implications for the technology advancement strategies both of new entrants to a market and of incumbents. First, compatibility is associated with the costs involved in switching away from incumbent technologies, because of an abundant or varied supply of complementary goods. Firms may influence perceptions of replaceability and the costs of switching not only by producing compatible technology but also by developing specific relationship routines and procedures and "technology-specific learning" (Heide and Weiss, 1995). Therefore, these mechanisms are worth studying in some detail, since they may have very different implications for the strategic behavior of firms involved in the industry. A strategy of advancing compatible technology may be successful in pursuing existing consumers to remain committed to a technology.

As the economy becomes more interconnected, issues of compatibility become more important in industries such as computers, telecommunications, and consumer electronics. The last decade has witnessed a shift from a focus on the value created by a single firm and product to an examination of the value created by networks of firms whose assets are commingled with those of external entities. Thus, managers seeking to expand the strategic reach of a company should quickly address the networks associated with the product. For example, the diffusion of high-definition television has largely depended on the complements network, allowing the television to not only broadcast programming as is commonly cited, but also other forms of digital input, such as those from DVD players (Heller, 2001). The creation of cmplementary resources (for instance, the greater availability of films in a VHS than in a Beta format) played a crucial role in boosting JVC’s VHS system, which in the end almost completely displaced Sony’s Betamax.

In our hypotheses, we assumed that the pace of technological change might be positively associated with switching costs. This is because the investments of consumers become obsolete under conditions of rapid technological change (Rosenberg, 1982). With technology-specific training, learning tends to grow with time, as consumers become more and more familiar with the existing technology. However, there may also be an effect of declining switching costs. With rapid sequential introductions of a product, consumers tends to get the impression that the improvements are marginal over time and that utility of improved versions will be quickly depreciated by sequential technological progress (Jackson, 1985; Shapiro and Varian, 1999). As MS Windows (a computer operating system) had already been upgraded many times (Windows 1.x, Windows 2.x, Windows 3.x, Windows 95, Windows 98, and Windows 2000), no significant relationship between the pace of technological change and switching costs can be observed.

Interestingly, the results we obtained for technology expertise should serve as a causality tale about the costs of switching in terms of highlighting the conditions under which a consumer’s commitment is likely to be high. Specifically, it is important for producers of incumbent technologies to be aware that the more expert a consumer has in technology the more likely he/she will be switch to a new technology, rather than rely on an existing one. Our findings can be used in guiding the marketing efforts of manufacturers. For example, as evidenced by the result, potential manufacturers of new technology should target expert consumers, because these consumers are more likely to switch to new technologies if they provide better functions.

This article is limited in the following ways. First of all, it suffers from the limitations of all cross-sectional design studies that attempt to observe an inherently dynamic phenomenon, such as technology commitment decisions in high-technology markets. One way of overcoming this limitation is by conducting a longitudinal study, in which consumer decision processes can be followed over time. Further, in the case of the early adoption of a technology (such as the PC vs. the Mac and VHS vs. Beta), the issue of momentum may be involved; once opinion leaders had decided on the PC or VHS in the early stages of the technology, most of the rest of the market followed along, and Mac and Beta were beaten. Once consumers are committed to a technology, then, all else being equal, switching out involves high costs. To recapitulate, there are difficulties with the cross-sectional data used in the current study. This study was conducted only in one country (Korea). In this regard, it can be also worthwhile to run this study in the rest of the world. We will leave these issues as a spur for further research.

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