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Blut M, Wang C, Schoefer K.

Factors Influencing the Acceptance of Self-Service Technologies: A Meta-

Analysis.

Journal of Service Research (2016)

DOI: http://dx.doi.org/10.1177/1094670516662352

Copyright:

This is the authors' accepted manuscript of an article that has been published in its final definitive form

by Sage Publications Ltd, 2016.

DOI link to article:

http://dx.doi.org/10.1177/1094670516662352

Date deposited:

28/10/2016

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Factors Influencing the Acceptance of Self-Service Technologies: A Meta-Analysis

Abstract

To facilitate efficient and effective service delivery, firms are introducing self-service

technologies (SSTs) at an increasing pace. This article presents a meta-analysis of the factors

influencing customer acceptance of SSTs. The authors develop a comprehensive causal

framework that integrates constructs and relationships from different technology acceptance

theories, and they use the framework to guide their meta-analysis of findings consolidated

from 96 previous empirical articles (representing 117 independent customer samples with a

cumulative sample size of 103,729 respondents). The meta-analysis reveals the following key

insights: (1) SST usage is influenced in a complex fashion by numerous predictors that

should be examined jointly; (2) ease of use and usefulness are key mediators, and studies

ignoring them may underestimate the importance of some predictors; (3) several determinants

of usefulness impact ease of use, and vice versa, thereby revealing crossover effects not

previously revealed; and (4) the links leading up to SST acceptance in the proposed

framework are moderated by SST type (transaction/self-help, kiosk/Internet, public/private,

hedonic/utilitarian) and country culture (power distance, individualism, masculinity,

uncertainty avoidance). Results from the meta-analysis offer managerial guidance for

effective implementation of SSTs, and provide directions for further research to augment

current knowledge of SST acceptance.

Keywords: Self-service technology, technology acceptance, meta-analysis

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Self-service technologies (SSTs) are “technological interfaces that enable customers

to produce a service independent of direct service employee involvement” (Meuter et al.

2000, p. 50). Service firms have strategically supplemented or even replaced traditional

“high-touch and low-tech” interpersonal encounters with “high-tech and low-touch” service

options. For example, banks provide customers with a variety of technology-based self-

service options, including ATMs, telephone banking, and Internet and mobile banking. SSTs

offer benefits to firms such as reducing labor costs (Bitner et al. 2002). However, firms will

not see the benefits unless sufficient numbers of customers adopt the technology.

This article presents a meta-analysis of the factors that influence customer acceptance

of SSTs. As such, we make three important contributions. First, previous SST studies use

various theories to develop acceptance models, such as the Technology Acceptance Model

(TAM; Davis et al. 1989) and the Unified Theory of Acceptance and Use of Technology

(UTAUT; Venkatesh et al. 2012). As a result, the constructs included in those models differ

significantly, so that factors impacting SST acceptance remain unclear. As the first attempt to

synthesize prior studies, we integrate constructs from various theories into a comprehensive

model of SST acceptance. Results document that our model outperforms any individual

theory, so that we confirm the value of the various theories as complementary perspectives,

and provide a more complete understanding of what factors influence SST acceptance and

how much influence each factor has.

Second, the literature is unclear about whether SST research should address

mediators. Some studies refer to TAM, which proposes that usefulness and ease of use fully

mediate the effects of several SST acceptance predictors (Oh et al. 2013), whereas other

studies hypothesize direct effects on SST use, as suggested by UTAUT (Jia et al. 2012). We

analyze mediation to determine how different antecedent factors influence SST acceptance.

Exploring the mediating role of usefulness and ease of use broadens understanding of both

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the predictors of SST acceptance and the mechanisms through which various predictors exert

influence. Establishing usefulness and ease of use as key mediators also provides managers

with a concise and actionable set of factors for influencing SST acceptance behavior.

Finally, empirical studies testing the influence of predictors show inconsistent

findings, ranging from significant effects to no effect at all. Such inconsistencies indicate that

the relevance of predictors may depend on the specific context (e.g., SST type). Accordingly,

SST research should investigate potential moderators in order to account for variations in

findings, though no systematic and comprehensive investigations of moderators have yet

emerged. Our moderator analysis resolves ambiguities and assesses generalizability of the

inconsistent results, offering insights into when different factors influence SST acceptance.

By examining differences in SST acceptance across cultures and technology types, we

provide managers with specific guidance about primary factors of concern when introducing

different types of SSTs in different cultures. Cross-cultural differences are of particular value

for service firms planning global rollout of SSTs.

LITERATURE REVIEW

Theories on Technology Acceptance

Several theories provide a conceptual foundation for SST acceptance studies. TAM

has been widely used to explain the acceptance of various technologies, including SSTs (Lin

et al. 2007). The theory postulates that perceived usefulness and ease of use determine

adoption and use of technology, and that other factors influence technology acceptance only

through these two determinants (Venkatesh and Bala 2008).

Though relatively new, the major technology acceptance theory UTAUT proposes a

different set of technology acceptance determinants for SST studies, to be incorporated either

explicitly or implicitly (Venkatesh et al. 2012). UTAUT posits that individual differences

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(e.g., age, gender) also influence technology acceptance. In practice, some studies include

individual differences as moderators (Weijters et al. 2007) while others use them as

determinants (Meuter et al. 2005).

Innovation Diffusion Theory (IDT) holds that an individual’s decision to adopt or

reject an innovation is determined by five major innovation characteristics: relative

advantage, complexity, observability, compatibility, and trialability (Rogers 1995). This

theory is relevant because, in service production and delivery, SST is often considered a

technological innovation. SST research has used IDT to investigate consumer acceptance

behavior, though less frequently than other theories (Walker et al. 2002).

Comparison of these theories reveals important similarities and differences that drive

and justify our conceptual development. Although different theories propose different sets of

acceptance determinants, we find that some determinants are conceptually very similar. Such

overlap indicates the critical importance of these determinants, but treating the similar

constructs as distinct complicates the literature. Venkatesh et al. (2003) call for moving

toward a comprehensive view of technology acceptance through review and synthesis of the

acceptance literature. Table 1 details definitions and theoretical roots of key constructs.

[Insert Table 1 here]

We also find differences among theories. While IDT focuses exclusively on

technology-related determinants, TAM and UTAUT incorporate user-related factors—

demographics and psychographics. Additionally, these theories propose different

relationships between technology acceptance and the determinants of acceptance. UTAUT

and IDT suggest that all determinants impact technology acceptance directly, whereas TAM

distinguishes between direct and indirect influences and proposes mediation mechanisms.

Recognition that each theory contributes to SST acceptance research is confirmation

that no single theory is entirely adequate. Therefore, although individual SST studies may

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have a reason to use a single theory, progressing toward a more complete view of SST

acceptance requires integration of different perspectives into a coherent framework. Existing

TAM meta-studies focus mainly on usefulness and ease of use as the drivers of technology

acceptance (King and He 2006; Schepers and Wetzels 2007), and they do not combine

predictors from different theories (Venkatesh et al. 2003).

Moderators in Technology Acceptance

Recent developments in technology acceptance theory identify several factors that

may moderate the influence of acceptance determinants. For example, TAM proposes that

user technology experience and voluntariness of use influence the effectiveness of some

determinants (Venkatesh and Bala 2008). UTAUT incorporates user age, gender, and

experience as additional moderators for technology acceptance (Venkatesh et al. 2012).

Empirical studies on technology acceptance also investigate moderators. For instance,

in their meta-analysis of TAM, King and He (2006) examine type of user and type of

technology use as moderators. Sun and Zhang (2006) propose that perceived usefulness is

more relevant for work-oriented technologies and ease of use has greater relevance for

entertainment-oriented technologies. In a study of hedonic and utilitarian technologies, van

der Heijden (2004) finds that enjoyment is more powerful for predicting user acceptance of

hedonic technologies than it is for utilitarian technologies. A culture-based meta-study on

TAM concludes that usefulness is more important in Western cultures, but ease of use

matters more in Eastern cultures (Schepers and Wetzels 2007). In their study of technology

acceptance, Cardon and Marshall (2008) observe mixed results for the moderating effect of

uncertainty avoidance as a specific cultural dimension.

While IS literature provides evidence that technology acceptance may depend on

technology type, user, or country culture, in SST research moderator analysis has only

recently become a focus. Collier et al. (2014) reveal that customer perceptions of control and

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convenience differ between public and private SSTs. Mortimer et al. (2015) test the SST

Intention to Use Model in Australia and Thailand, and find that the model does not hold

across the two countries. The limited availability of research on technology and culture

moderators may be because comparing different technologies and countries requires

comprehensive data sets that often can only be found in meta-studies.

By examining two sets of moderators—technology type and country culture—our

study establishes important distinctions from prior moderator studies. First, rather than

focusing on one specific cultural dimension or contrasting West vs. East generally, we

examine all of Hofstede’s (2001) cultural dimensions. We also look at the moderating effects

of cultural dimensions that have not yet been tested in technology acceptance studies.

Second, we extend the studies of hedonic vs. utilitarian technology and public vs. private

technology with investigations contrasting kiosk and Internet technology, and transaction and

self-help technology. Third, we move our moderator tests beyond the predictors of perceived

usefulness and ease of use targeted in TAM studies. Finally, building on the TAM meta-

studies that combine diverse technologies, our meta-study examines moderating effects in the

specific context of self-service technology.

CONCEPTUAL MODEL AND HYPOTHESES

The conceptual framework guiding this meta-analysis is illustrated in Figure 1. To

develop the framework, we reviewed SST literature and technology acceptance theories

regarding (1) potential determinants of SST use, (2) mediators and outcomes, and (3)

contextual moderators. First, we selected relevant determinants from key acceptance theories

and created four groups based on the underlying theory. Second, consistent with TAM, we

hypothesized ease of use and usefulness as two key mediators. We also included attitude

toward use as a mediator, as proposed in the original TAM (Davis et al. 1989) and as used in

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models of several SST studies. However, because the relationships among the three mediators

are well established and supported in the literature, we did not derive hypotheses for them.

Third, to account for possible contextual differences across studies, we examined cultural

dimensions and technology types as moderator sets. Research design and sample composition

served as control variables.

[Insert Figure 1 here]

Our hypotheses of determinant influences on SST acceptance developed from key

acceptance theories (TAM, UTAUT, IDT) and findings in the SST literature. In cases where

SST research finds additional relationships that are not proposed in those theories, we also

included them in our hypotheses. When SST research and acceptance theories differed in how

they position variables (e.g., demographics are treated as determinants in SST studies, and as

moderators in UTAUT), our hypotheses followed SST research models (i.e., we treated

demographics as determinants rather than moderators). Tables A and B in Web Appendix A

detail our theoretical justification and empirical support for various determinants on SST

acceptance.

Effects of TAM/UTAUT Determinants on SST Acceptance

Usefulness. Consumers perceive SSTs to be useful when they save time/costs, and

when they are convenient (Ding et al. 2007). To specify the causal link between usefulness

perception and usage intention, TAM refers to the theory of reasoned action (Venkatesh

2000). TAM assumes that individuals who believe a technology will be useful are more likely

to display positive behavioral intentions.

Ease of Use. Referring to the theory of reasoned action, TAM proposes that when

customers perceive a technology as simple to use, they are more likely to use it (Gelbrich and

Sattler 2014). TAM also argues that ease of use is a direct determinant of usefulness (Davis et

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al. 1989) because the less effort a technology requires, the more likely that use of the

technology will increase task performance.

Subjective Norm. TAM suggests that the subjective norm has a direct effect on usage

intention. The rationale is that people may intend to perform a behavior, even if they are not

themselves favorable toward the behavior or its consequences, if they believe that one or

more important referent individuals approve the behavior. Furthermore, TAM argues that

when important referents communicate a belief in SST usefulness, people can change their

own beliefs in agreement.

External Control. For users of new technologies such as SSTs, prior technology

introductions impact perceptions of external control. According to TAM, these general

perceptions are technology-independent and serve as situational anchors in the formation of

perceived ease of use (Venkatesh 2000). This theory assumes that, lacking substantial

knowledge of the new technology, customers base their perceptions of the technology’s ease

of use on generalized abstract criteria. While the other predictors impact only usage intention,

UTAUT holds that external control also determines usage behavior. UTAUT explains that

external control acts similarly to perceived behavioral control in the theory of planned

behavior, thereby influencing intention and behavior.

Enjoyment. TAM proposes that when technology-specific enjoyment increases, the

salience of perceived ease of use also increases as a determinant of intention. Accordingly, a

stronger perception of technology use as enjoyable may increase perceived ease of use for the

target technology. UTAUT also suggests that enjoyment is a critical determinant of

behavioral intention, independent of ease of use perceptions (Venkatesh et al. 2012). Thus,

H1a: Usefulness has a positive impact on usage intention.

H1b: Ease of use has a positive impact on usefulness and usage intention.

H1c: Subjective norm has a positive impact on usefulness and usage intention.

H1d: External control has a positive impact on ease of use, usage intention, and usage

behavior.

H1e: Enjoyment has a positive impact on ease of use and usage intention.

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Effects of TAM Determinants on SST Acceptance

Image. As an extension of TAM, Venkatesh and Davis (2000) propose a positive

relationship between the perceived image of a technology and its usefulness. They argue that

image enhancement and associated social support are significant influences for individuals

who perceive technology use as a means of improving task performance.

Result Demonstrability. TAM argues that when task performance gains are not readily

attributed to use of the technology, even effective technologies can fail to gain user

acceptance. Therefore, result demonstrability positively influences perceived usefulness. In

addition, SST studies propose that result demonstrability directly influences usage intention

and behavior (Meuter et al. 2005). It is argued that opportunity to observe and communicate

with others about an SST increases the chance that it will be used.

Self-Efficacy. TAM indicates that self-efficacy is linked to perceived ease of use

(Venkatesh and Davis 1996). When users have direct experience with a technology, their

general confidence in technology knowledge and ability is the basis for judging ease of use

for new technology. The SST literature also proposes a direct effect of self-efficacy on usage

intention and behavior. Meuter et al. (2005) argue that in technology-mediated environments

the perceived confidence in ability to engage in a task influences the likelihood of technology

use.

Anxiety. According to TAM, computer anxiety negatively influences perceived ease

of use (Venkatesh 2000). Classic anxiety theories propose that anxiety negatively impacts

cognitive responses, particularly process expectancies. An underlying assumption is that

individuals with high computer anxiety are more likely to negatively assess the process of

using technology. According to the SST literature, anxiety may lead to technology avoidance

behavior and to greater reluctance to use an SST.

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Computer Playfulness. While enjoyment is related to direct experiences with a

specific technology, computer playfulness is related to general perceptions about technology

use. Playfulness represents an abstraction of openness to the process of using technologies,

and it serves as an anchor for perceived ease of use for a new technology (Venkatesh 2000).

TAM argues that those customers who are generally more “playful” with technologies can be

expected to use a new technology simply for the sake of using it, rather than for specific

positive outcomes associated with its use. Because they enjoy the process itself, these playful

individuals may tend to underestimate the difficulty of the means or process of using a new

technology. Therefore,

H2a: Image has a positive impact on usefulness.

H2b: Result demonstrability has a positive impact on usefulness, usage intention, and

usage behavior.

H2c: Self-efficacy has a positive impact on ease of use, usage intention, and usage

behavior.

H2d: Anxiety has a negative impact on ease of use, usage intention, and usage

behavior.

H2e: Computer playfulness has a positive impact on ease of use.

Effects of UTAUT Determinants on SST Acceptance

Habit. UTAUT proposes direct effects of habit on usage behavior and intention

(Venkatesh et al. 2012). This theory presumes that repeated performance of a behavior

produces habituation, and that behavior can be directly activated by stimulus cues. In this

way, a repeated similar situation can be sufficient to trigger an automatic response.

Age. Previous studies on the adoption of innovations have examined demographic

characteristics (Rogers 1995). Older people are more likely to encounter difficulty in

processing new or complex information, which affects their ability to learn new technologies.

Therefore, they are less likely to use new technologies.

Gender. Meuter et al. (2005) suggest that men are generally more interested in

technology than women, and therefore use technology more frequently. Prior SST studies

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also provide evidence for a significant direct relationship between customer gender and SST

acceptance (Ding et al. 2007).

Experience. Heavy users of technologies are more confident in their ability to use the

technology, and are therefore more likely to try SSTs (Meuter et al. 2005). The SST literature

provides further evidence that experience increases ease of use and usefulness perceptions

(Gefen et al. 2003; Karahanna et al. 1999). Because hands-on experience with technology

increases knowledge and confidence, experienced customers perceive a technology to be

easier to use than do customers with less experience (Hackbarth, Grover, and Yi 2003). The

literature also suggests that consumers who are more experienced with an SST may

understand how to use it to better advantage, resulting in a stronger belief in its usefulness.

Hence,

H3a: Habit has a positive impact on usage intention and usage behavior.

H3b: Age has an impact on usage behavior, such that younger people are more likely

to use SSTs than older people.

H3c: Gender has an impact on usage behavior, such that men are more likely to use

SSTs than women.

H3d: Experience has a positive impact on ease of use, usefulness, and usage behavior.

Effects of Other Determinants on SST Acceptance

Compatibility. IDT argues that increased compatibility with personal values/lifestyle

increases the odds of trying an SST (Moore and Benbasat 1991). Because adoption of an

incompatible innovation requires prior adoption of a new value system, which is a slow

process, compatibility is positively related to SST usage intention and behavior (Meuter et al.

2005). To date, however, IDT predictors have received little attention in the SST literature.

Trialability. The SST literature supports a direct effect of trialability on usage by

referring to IDT (Rogers 1995). This theory suggests that trials of innovations can reduce

uncertainty for potential adopters, with particular importance in the early stages of SST use.

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As well, the literature indicates that the opportunity to observe other SST users (indirect trial)

also influences usage intention (Eastlick 1996).

Risk. The SST literature also proposes that risk has an impact on adoption decisions

(Meuter et al. 2005). Using an SST often involves some level of potential risk to customers,

such as a transaction failure due to technical or human error. Thus, customers’ perception that

an SST is likely to malfunction lowers their intention to use the technology and prompts a

turn to personal service (Curran and Meuter 2005). Risk, therefore, negatively impacts SST

usage intention and behavior.

Technology Readiness. While technology anxiety focuses specifically on users’

negative state of mind regarding ability and willingness to use technology, technology

readiness is a broad, trait-like construct, focusing on such issues as innovativeness and the

tendency to be a technology pioneer (Parasuraman and Colby 2015). Customers who are

highly technology ready are more likely to try a technology. They are also assumed to have

fewer problems exploring technology benefits, and find the technology less difficult to use

(Chen et al. 2009). Thus, this predictor reveals a link to ease of use, usefulness, usage

intention, and usage behavior.

Need for Interaction. The presence of contact between customers and service staff is a

key difference between personal service and an SST, indicating that the self-service

experience is inherently tied to need for interaction. This predictor is infrequently examined

in SST studies, though customer needs research shows that needs influence decision making.

Customers with a need for interaction find SST technology less useful, demonstrate less

willingness to use it, and show a greater likelihood of avoiding it (Curran and Meuter 2005).

Thus,

H4a: Compatibility has a positive impact on usage intention and usage behavior.

H4b: Trialability has a positive impact on usage intention and usage behavior.

H4c: Risk has a negative impact on usage intention and usage behavior.

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H4d: Technology readiness has a positive impact on ease of use, usefulness, usage

intention, and usage behavior.

H4e: Need for interaction has a negative impact on usefulness, usage intention, and

usage behavior.

Moderating Effects of Cultural Dimensions

Power Distance. Power distance belief is the extent to which people accept inequality

in a system (Hofstede 2001). In high power distance cultures, customers place greater

reliance on the more powerful members of society than they do on themselves, and they

expect the powerful members to provide support and structure (Hofstede 2001). In this

condition, predictors related to support have more importance, while predictors associated

with a person’s own capabilities lose importance. Service providers can support customers

through organizational and technical resources (external control), and through availability of

service employees (need for interaction). In cultures where customers are expected to have

greater reliance on the SST firm, individuals are less likely to rely on assessments of their

own capabilities (self-efficacy) and past experiences. Thus,

H5: For SST acceptance, the influences of (a) external control and (b) need for

interaction are stronger in high power distance cultures; the influences of (c)

self-efficacy and (d) experience are stronger in low power distance cultures.

Individualism–Collectivism. Individualism refers to the extent to which people in a

country prefer to act independently (individualism), in contrast to interdependent action

(collectivism) (Steenkamp and Geyskens 2006). Customers in individualistic societies “place

their personal goals, motivations, and desires ahead of those of others, whereas collectivistic

cultures are conformity-oriented and show a higher degree of group behavior and concern to

promote their continued existence” (Steenkamp and Geyskens 2006, p. 139). In

individualistic societies, a person’s attitude and behavior are strongly regulated by individual

preferences and less so by group needs (Hofstede 2001). Predictors related to personal needs

gain importance in individualistic cultures, while predictors related to group needs and effort

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associated with need satisfaction lose importance. As a predictor for SST use, enjoyment is

more relevant in individualistic cultures because it strongly relates to the fulfillment of

personal needs. Persons in individualistic cultures are less likely to complain about effort and

anxiety for technology that satisfies their personal needs. Thus, self-efficacy and anxiety are

less relevant in individualistic cultures. In collectivistic cultures, people care more about the

collective wellbeing, and are therefore more likely to be influenced by subjective norm and

need for interaction. Hence,

H6: For SST acceptance, the influence of (a) enjoyment is stronger in individualistic

cultures; the influences of (b) subjective norm, (c) self-efficacy, (d) anxiety, and

(e) need for interaction are stronger in collectivistic cultures.

Masculinity–Femininity. The cultural dimension that encompasses masculinity and

femininity captures the extent to which “tough” (masculine) values prevail over “tender”

(feminine) values in a society (Hofstede, Hofstede, and Minkov 2010). Masculinity is

described as an agentic orientation (He et al. 2008), demonstrating characteristics of

assertiveness, competitiveness, focus on maximizing upsides, and functional orientation. In

contrast, a feminine orientation—or a communal orientation—shows characteristics of

reciprocity, relational values, benevolence, focus on minimizing downsides, and experiential

orientation. Predictors related to “tough” values in society would therefore gain importance in

masculine culture, while predictors related to “tender” values would lose relevance. In the

context of SST acceptance, customers in masculine cultures are more likely to rely on their

own abilities (self-efficacy) and their own past experiences than are customers in feminine

cultures, who are more likely to appreciate exchange with others (subjective norm, need for

interaction). Experientially orientated feminine cultures show enjoyment to have a greater

effect. The literature presents masculine cultures as more willing to take risks, making risk

less relevant as a predictor. Hence,

H7: For SST acceptance, the influences of (a) self-efficacy and (b) experience are

stronger in masculine cultures; the influences of (c) subjective norm, (d) need

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for interaction, (e) enjoyment, and (f) risk are stronger in feminine cultures.

Uncertainty Avoidance. Uncertainty avoidance refers to “the extent to which the

members of a culture feel threatened by uncertain or unknown situations” (Hofstede 2001, p.

161). Individuals in high uncertainty avoidance cultures embrace predictability and avoid

ambiguity. We assume that predictors capable of reducing risk are more important in high

uncertainty avoidance cultures, while predictors lose relevance in these cultures that are

assessed as less capable. In high uncertainty avoidance cultures, customers are more likely to

reduce risk by relying on recommendations from other customers (subjective norm).

Additionally, customers with greater computer playfulness are more likely to reduce risk by

interacting directly with technology and gaining direct experience with the technology.

Subjective norm, computer playfulness, and experience are more important in these cultures.

Customers conventionally believe non-provider information sources (friends, personal

experience with technology) to be particularly reliable, and should, therefore, use these

information sources more often than others. Technology-related information (ease of use,

usefulness) and provider-related information (interaction with service employees) should be

less relevant for customers in high uncertainty avoidance cultures who demonstrate more

skepticism and greater tendency to rely on friends and family for information. Hence,

H8: For SST acceptance, the influences of (a) subjective norm, (b) experience, and

(c) computer playfulness are stronger in cultures with high uncertainty

avoidance; the influences of (d) usefulness, (e) ease of use, and (f) need for

interaction are stronger in cultures with low uncertainty avoidance.

Moderating Effects of SST Types

Transaction vs. Self-Help SSTs. We distinguish between transaction SSTs that are for

direct transactions (e.g., online payment) and self-help SSTs for other self-help purposes

(e.g., airport self-check-in kiosks). These technologies differ regarding their extent of process

standardization and potential negative consequences of use (Goodhue 1995, Meuter et al.

2000). Process standardization influences relevance of prior experiences and need to rely on

16

others or on well-designed technology interfaces. Thus, predictors related to prior experience

gain importance for technologies with standardized processes, while technology-related

predictors and support lose relevance. Transaction SSTs have a more standardized service

process. For example, the online payment procedure is similar for many commercial

websites. Therefore, consumers’ previous experience should be highly relevant for adoption

of a new transaction SST, and external technology characteristics such as usefulness, ease of

use, and external control should be less important. Self-help SSTs usually have different

procedures for different services, even within the same technology. The process for using

online banking to open an account is quite different from the process for updating personal

information at the same bank. In this situation, we expect past experience to lose relevance

and acceptance of an SST to be driven by external characteristics of the focal technology. IS

studies suggest that ease of use and sense of control are especially important if users are to

accept a system with non-routine tasks. Because users typically acquire new information

from existing technology, they are more likely to be frustrated by frequently encountering

unfamiliar processes (Goodhue 1995).

Risk-related predictors are also assumed differ in relevance, and have greater

importance for technologies with more severe negative consequences. Because of the

potential financial risk associated with transaction SSTs, risk may still be important as a

technology characteristic. To minimize such risk, we expect that consumers will desire

availability of personal assistance, even if they do not typically enjoy interacting with service

staff. Thus, we expect the negative effects of risk and need for interaction on technology

acceptance to be stronger for transaction SSTs. Hence,

H9: For SST acceptance, the influences of (a) experience, (b) risk, and (c) need for

interaction are stronger for transaction SSTs; the influences of (d) usefulness,

(e) ease of use, and (f) external control are stronger for self-help SSTs.

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Kiosk vs. Internet SSTs. According to Dabholkar (1994), kiosk SSTs are provider-

based and Internet SSTs are primarily customer-based. In provider-based SSTs, the service

provider establishes access technology and sets up specific terminals, such as ATMs or

check-in kiosks. Alternatively, users can access customer-based SSTs through their own

technological devices, such as a PC or smartphone. These technologies differ in several

aspects, including connectivity/interactivity, system security, and physical appearance. We

assume that predictors related to these characteristics gain importance for the respective

technology.

We expect that subjective norm plays a more important role in driving consumer

acceptance of Internet-based SSTs, because many of these technologies have now been

integrated into social networking sites and apps that connect people and social groups. To

live up to the expectations of their social groups, consumers will be highly motivated to use

the Internet-based social SSTs. Moreover, Curran and Meuter (2005) find that risk affects

adoption of online banking but does not influence ATM use. In line with this finding, online

data security concerns lead to a belief that Internet SSTs are generally riskier to use than

kiosk SSTs. For kiosks, the infrastructure and security measures imply service provider

oversight for risk. In the case of Internet SSTs, however, the lack of immediate personal

support increases consumers’ perception of risk. The impact of playfulness on consumer

acceptance is expected to have a greater impact for kiosk SSTs. Compared with Internet SSTs

with the same interface, kiosk SSTs vary significantly in terms of physical appearance, which

may satisfy the consumer need to be spontaneous during use. Therefore,

H10: For SST acceptance, the influences of (a) subjective normand (b) risk are

stronger for Internet SSTs; the influence of (c) computer playfulness is stronger

for kiosk SSTs.

Public vs. Private SSTs. SST firms can now provide both onsite and offsite

technology (Dabholkar and Bagozzi 2002). Public SSTs are onsite technologies located

18

where social interaction between the customer and other patrons can take place (e.g., ATMs,

pay-at-the-pump gasoline terminals). These public technologies provide different levels of

customer visibility, so that predictors related to potential embarrassment of individual users

gain importance. Offsite technologies are private SSTs located where a customer can interact

with a technology but does not interact with others (e.g., online banking at home). Customers

using public SSTs typically do not want to delay other customers, or to be embarrassed

themselves, and so need assurance of quick and successful SST use (Gelbrich and Sattler

2014). This assurance develops from a perception that the technology is easy to use, or from

the customer’s past experience. We also expect that the inhibiting effect of anxiety may be

enhanced in a public situation. Persons who are anxious using technology tend to avoid SSTs

generally, and the social pressure of using a public SST makes acceptance even less likely.

For the same reason, we also suggest that risk is more important in driving acceptance for

public SSTs than for private SSTs. Therefore,

H11: For SST acceptance, the influences of (a) ease of use, (b) anxiety, (c)

experience, and (d) risk are stronger for public SSTs.

Hedonic vs. Utilitarian SSTs. Finally, we distinguish between hedonic SSTs that

provide hedonic services (e.g., self-serve yogurt) and utilitarian SSTs that provide utilitarian

services (e.g., ATMs, grocery checkout machines). These technologies differ, however, with

regard to the type of benefits that they provide to users. Motivation theory, useful for

evaluating hedonic and utilitarian SSTs, posits that human behavior is driven by two types of

motivation—extrinsic and intrinsic (Deci 1975). Our model represents extrinsic motivation

through usefulness, ease of use, and subjective norm, which focus on instrumental benefits

external to the use of an SST. Alternatively, the model indicates intrinsic motivation through

computer playfulness and enjoyment, which focus on inherent pleasure and satisfaction

derived from SST use. We expect that extrinsic motivators are more important in driving the

acceptance of utilitarian SSTs because consumers often use these SSTs to achieve an external

19

goal. We further expect that intrinsic motivators are more important in driving acceptance of

hedonic SSTs because, for consumers who use such SSTs mainly to enjoy the service

experience, a fun and playful process is important. Therefore,

H12: For SST acceptance, the influences of (a) computer playfulness and (b)

enjoyment are stronger for hedonic SSTs; the influences of (c) usefulness, (d)

ease of use, and (e) subjective norm are stronger for utilitarian SSTs.

METHOD

Data Collection and Coding

We began the literature search using electronic databases such as ABI/INFORM,

Proquest, Scopus, Web of Science, Google Scholar, and EBSCO (Business Source Premier).

As well, we conducted additional web searches to produce a comprehensive list of empirical

studies, and manually reviewed numerous journals and reference lists of collected studies.

We contacted authors and requested unpublished studies. In total, we collected 96 usable

articles published between 1994 and 2015 (Web Appendix C). We employed two

independent coders to code study characteristics, with an agreement rate of 96%. Coders used

the construct definitions in Table 1 to classify variables.

Integration of Effect Sizes

For our research, we used correlation coefficients as effect sizes. When such

information was not available, we transformed regression coefficients to correlations

(Peterson and Brown 2005). If we realized during coding that some samples contained more

than one correlation on the same association between two constructs (e.g., due to the use of

multiple measures of the same construct), we calculated the average across these correlations

and reported the data as a single study (Hunter and Schmidt 2004). In total, we gathered

1,306 effect sizes extracted from 117 independent samples that we extracted from 96 articles.

The combined sample includes 103,729 respondents.

20

To calculate averaged correlations, we employed the random-effects approach

suggested by Hunter and Schmidt (2004). As well, we corrected correlations for measurement

error in both dependent and independent variables using reliability coefficients. We divided

the correlations by the product of the square root of the respective reliabilities of the two

involved constructs (Hunter and Schmidt 2004). Our method also corrected for the

dichotomization of a continuous dependent variable and independent variable, as well as for

range restriction. We weighted the artifact-adjusted correlations by sample size to adjust for

sampling error, after which we calculated standard errors and 95% confidence intervals for

each sample size-weighted and artifact-adjusted correlation.

We assessed the homogeneity of the effect size distribution and the need for

moderator analysis using a Chi2 test of homogeneity and the 75% rule of thumb (Hunter and

Schmidt 2004). To assess the robustness of our results and to evaluate potential publication

bias, we calculated Rosenthal’s (1979) fail-safe N (FSN; also referred to as File-drawer N),

which refers to the number of studies averaging null results needed to bring significant

relationships to a barely significant level (p=.05). Finally, we calculated the statistical power

of our test, as suggested by van Vaerenbergh et al. (2014).

Evaluation of Causal Model

We used structural equation modeling to test mediating effects, and we calculated a

complete correlation matrix, including the effect sizes of all variables that the collected

studies examined most often. To test our model, the correlation matrix was used as input to

LISREL 8.80. We used the harmonic mean of all sample sizes (N=1,730) as the sample size

in our analyses.

Moderator Analysis

For our analysis, we assessed the effect of moderators using multivariate, multilevel

meta-regressions. Hox (2010) argues that studies reporting multiple measurements are

21

unlikely to be considered independent of one another. We calculated 12 multilevel models—

one for each determinant. Similar to van Vaerenbergh et al. (2014), the reliability-corrected

correlations served as the dependent variable, and we regressed the correlations on fourteen

variables, including cultural variables, SST-type variables, and method variables. We also

included four dummy variables to represent each variable that correlated with one of the

predictors (e.g., ease of use; van Vaerenbergh et al. 2014).

A detailed description of our coding, integration, and analysis procedures can be

found in Web Appendix B.

RESULTS

Descriptive Statistics

As displayed in Table 2, the strongest effect sizes can be observed for the

TAM/UTAUT and TAM predictors, but predictors from UTAUT and other theories were

also significant.

TAM/UTAUT determinants. In line with Hypotheses H1a – H1e, we observe that all

TAM/UTAUT predictors are related to usage intention, including usefulness, ease of use,

subjective norm, external control, and enjoyment. Interestingly, we also find all predictors to

be related to usage behavior, except for external control. While external control is related to

intention, it is not related to usage behavior, which contradicts our assumption in Hypothesis

H1d. This predictor impacts usage behavior indirectly because it is a weak proxy for actual

behavioral control (Venkatesh et al. 2012).

TAM determinants. The results for TAM predictors support theorized effects for self-

efficacy and anxiety, as proposed in Hypotheses H2c – H2d. In addition, we find that

computer playfulness has a direct effect of on usage intention, which we did not anticipate.

According to attitude/intention theories, beliefs about an innovation are related to behavioral

22

intentions. The influence of result demonstrability on behavior proposed in H2b lacked effect

sizes and could not be tested. We do, however, find support for usage intention.

UTAUT determinants. The UTAUT predictors include habit, age, gender, and

experience. While experience is related to usage behavior, which supports H3d, habit is

related to intention but not behavior, giving partial support for H3a. The demographic

characteristics were insignificant, so Hypotheses H3b – H3c are rejected. The SST literature

discusses demographic variables as a much weaker predictor compared to other predictors

such as habit and experience (Meuter et al. 2005).

Other determinants. While Hypotheses H4a – H4b, associated with compatibility and

trialability, could not be tested because they lack effect sizes for usage behavior, we find that

these predictors impact usage intention and so support the hypotheses. Hypothesis H4c is

rejected because risk is not related to usage intention but is positively related to usage

behavior. Attentional Control Theory (ACT; Eysenck et al. 2007) suggests that for less

complex tasks such as SST usage, individuals’ concerns can motivate them to improve their

task performance to avoid failure and negative evaluation, thereby implying a positive

association between risk and SST usage.

We also find support for H4d – H4e. Though technology readiness is related to usage

behavior and intention, need for interaction is unrelated to behavior and exerts only indirect

influence through intention.

[Insert Table 2 here]

We determined all significant relationships to be robust against publication bias. In

most cases, significant Chi2 tests of homogeneity suggest moderator analysis. The employed

tests have sufficient power (Web Appendix B). We also used descriptive statistics to examine

the impact of predictors on mediators (Table C, Web Appendix A). Results indicate that of

23

the 40 predictor-mediator relationships we examined, 28 relationships (70%) are significant;

thus, we test mediating effects in the causal model.

Results of Causal Model

We calculated the SEM for all constructs for which we could derive a complete

correlation matrix (Table 3), and tested the proposed model against TAM and UTAUT

(Figure 2). Although our model outperforms these theories, modification indices suggest

inclusion of further relationships (Table 4). The causal model provides insights on mediating

mechanisms as well as additional relationships to consider.

[Insert Figure 2 and Tables 3 – 4 here]

First, we find that usefulness represents a partial mediator and should be considered in

future studies. Seven of eight tested predictors are related to usefulness of the SST. As well,

usefulness is significantly related to attitude toward using the SST, usage intention, and usage

behavior. The calculated relative importance (47%) underscores the relevance of this

mediator (Table D, Web Appendix A).

Second, we find that ease of use represents a partial mediator. Because all predictors

are related to ease of use—which is related to usefulness, attitude, intention, and behavior—

future SST studies should address this mediator.

Third, the SEM indicates existence of crossover effects that have not been discussed

in the literature. We find that predictors of ease of use and usefulness do not influence one

mediator exclusively. With regard to usefulness, we find external control, self-efficacy, and

computer playfulness to be significant predictors, and conclude that confidence in personal

abilities provides more accurate assessment of usefulness. Customers who enjoy interaction

with computers, therefore, are more likely to appreciate the benefits of SSTs. For ease of use,

we see that subjective norm and need for interaction are related. Strong subjective norms

appear to motivate customers to adapt ease of use beliefs of key referents. Also, customers

24

with greater need for interaction tend to assess ease of use more negatively. Without the

social support, they exhibit less confidence for independent use.

Finally, the SEM shows further important relationships. Contrary to H2d, we find that

anxiety is positively related to usage behavior and is unrelated to usage intention. As we saw

for risk perception, anxious individuals try avoiding failure and embarrassment, which in turn

may motivate them to improve their task performance (i.e. use SSTs successfully).

Furthermore, computer playfulness shows a strong negative effect on ease of use, which is

contrary to H2e. Customers who enjoy spending time with computers tend to have

experienced more varied technologies; thus, they may be more demanding with respect to

effort needed to use the technology. Similarly, results showing that customer experience

negatively impacts usage behavior and ease of use counter our prediction in H3d. This

finding indicates that more experience may also lead to higher customer demands. Finally,

need for interaction is positively associated with usefulness (H4e). Customers who prefer

personal service often demonstrate less understanding of the benefits of the technology and

may overestimate its usefulness.

Results of Moderator Analysis

Cultural moderators. We find support for most moderating effects of cultural

dimensions, which largely supports Hypotheses H5 – H8 (Tables 5 and 6). Only three

moderating effects show a direction contrary to our predictions. In H5b, the need for

interaction is weaker in high power distance cultures because firms are less considerate of

customers (Hofstede 2001). Furthermore, and contrary to our prediction in H7c, the

subjective norm is stronger in masculine cultures. This condition can be explained by the

heightened functionality orientation of masculine cultures that is mirrored in the functionality

characteristics of SSTs and other technologies. Contradicting H7d, the need for interaction is

25

stronger in masculine cultures. The greater functional orientation of these cultures motivates

customers to appreciate information exchange about functional aspects of the technology.

Beyond our hypotheses, we observe further moderating effects. First, enjoyment is

more relevant in higher power distance cultures. These individuals expect more support from

a firm, which allows them to care less about their own capabilities. As a result, they have

increased appreciation for positive aspects of SST such as enjoyment. Second, compatibility

is less important in individualistic countries, where persons tend to be driven by achievement.

Such customers will apply more effort to using the SST, showing less concern for

compatibility with values and lifestyle. Third, self-efficacy is less relevant in high uncertainty

cultures. These customers rely more on external quality cues for decision making, giving less

consideration to internal factors such as self-efficacy.

[Insert Tables 5 and 6 here]

Technology moderators. Our results lend support for the moderating effects of SST

types, as proposed in H8 – H9. Observations in Tables 5 and 6 detail effects of SST types that

we did not hypothesize. First, we find that both usefulness and experience are more important

for Internet SSTs than they are for kiosk SSTs. Because kiosks are used infrequently,

customers are willing to accept processes that take more time and that are more complex, and

they find prior experience more valuable. Second, we observe that external control is more

important for hedonic SSTs, while self-efficacy and experience are more relevant for

utilitarian SSTs. To ensure a pleasurable service experience, customers of hedonic SSTs rely

more on company support, while for utilitarian SSTs the customers are more willing to

contribute more effort themselves.

Method moderators. When comparing the results of method moderators, we find little

evidence for systematic differences across different research designs and sampling

approaches (Table 5).

26

GENERAL DISCUSSION

This study aims to provide a comprehensive and coherent understanding of

consumers’ SST use by applying a meta-analytic technique to synthesize previous research.

To accomplish this, we develop a comprehensive theoretical model of SST acceptance by

integrating theories, constructs, and relationships from prior studies. Empirical results support

our model and provide insights into what factors influence SST acceptance, as well as

demonstrating how and when those factors exert their influence.

First, we find that all TAM/UTAUT determinants and most TAM determinants drive

SST acceptance. Results for UTAUT and other theory determinants are mixed, however. We

had anticipated that influences of age and gender would be insignificant, as prior studies

indicate that individual differences have a limited or weak impact on SST adoption

(Dabholkar 1996). Therefore, our results suggest that demographic variables are not effective

predictors of SST acceptance, and that they are better used as control/moderator variables—

as is the case for UTAUT.

Relative impact varies for the significant determinants, suggesting that not all

determinants have equal importance in driving SST acceptance. The most influential

predictors are usefulness, ease of use, subjective norm, enjoyment, self-efficacy,

compatibility, trialability, and technology readiness. These results indicate that every theory

contributes to predicting SST acceptance behavior, but no single theory is sufficient, thus

providing strong justification for integrating these theories into our comprehensive theoretical

model. Empirical testing also confirms that our model outperforms the individual use of

TAM or UTAUT (Table 4).

Second, we use SEM to test the mediating roles of usefulness and ease of use. With

few exceptions (Meuter et al. 2005), mediators have not been explicitly examined in previous

27

SST research. However, mediators can deepen our understanding about the processes

involved in SST acceptance. We follow TAM for our model, examining usefulness and ease

of use as key mediators. The results show partial mediation in nearly all relationships (not

limited to those proposed in TAM), although usefulness and ease of use mediate different

determinants. For example, while our bivariate analysis finds experience to be a positive

influence for SST acceptance, our SEM-based mediator analysis reveals that the underlying

mechanism of that positive effect may be usefulness perception. As a consequence,

experienced consumers can better understand how to gain more advantages from an SST,

which may motivate use of the technology.

In contrast to Venkatesh and Bala’s (2008) hypothesis and results on TAM, we find

evidence for crossover effects, as some usefulness determinants impact ease of use, and vice

versa. This is not limited to TAM determinants, indicating that the mediating mechanisms

usefulness and ease of use do not work separately, as suggested by TAM, and that some

determinants may influence SST acceptance through both mediators. Examination of these

crossover effects helps to develop a more comprehensive nomological network around key

acceptance theories.

Third, our moderator analysis reveals that SST type influences the strength of

relationships between SST acceptance and its determinants. This result provides new insights

regarding conditions in which determinants influence SST acceptance. For example, prior

observations regarding the role of enjoyment are inconsistent, with many studies reporting a

weak or insignificant influence on technology acceptance (Davis et al. 1992), while others

find a strong impact (Collier and Barnes 2015). Our results suggesting that the influence of

enjoyment is stronger for hedonic SSTs and weaker for utilitarian SSTs offer an explanation

for previous mixed findings, thereby advancing a broader understanding of SST acceptance.

28

Fourth, we also find that a country’s culture represents an important moderator. Many

SST acceptance models have been developed in the U.S., but we show that it is necessary to

develop cultural adaptations for these models before applying them to global markets. Our

study confirms that cultural dimensions alter the effectiveness of acceptance factors for SSTs

introduced in different countries. Thus, we extend SST acceptance research by considering

how it is shaped by culture (Table 6).

MANAGERIAL IMPLICATIONS

For practitioners, this meta-analytical research also has several implications. First, our

results show that demographic variables such as age and gender are not effective predictors of

SST acceptance, and should therefore be avoided as segmentation variables for SST

acceptance (Table 1). The individual-level characteristic of technology readiness represents a

more promising method. This approach agrees with Parasuraman and Colby (2015), who

recently used a streamlined technology index to segment customers. Drawing on their work,

and consistent with our finding that technology readiness matters for SST acceptance, we

recommend that when firms introduce an SST to a market, they initially target “explorers”

(who have higher degrees of motivation and lower levels of resistance) and “pioneers” (who

tend to hold strong positive views about technology).

Second, our mediation analysis results enable practitioners to better appreciate both

direct and indirect ways in which SST determinants influence SST acceptance. In particular,

our results reveal the important roles of usefulness and ease of use in translating the effects of

determinants on SST acceptance. Using this insight, firms may (a) decide to launch a

marketing communication campaign geared toward increasing SST usefulness perceptions,

and (b) improve customers’ SST acceptance by developing SST interfaces that are more

intuitive. Because this is as much a technical challenge as it is a marketing communications

29

challenge, firms must ensure the close cooperation of customer service, IT, product

development, and marketing communications departments.

Third, practitioners should realize that the importance of SST acceptance predictors is

context-specific. In particular, results indicate that service firms are better positioned to

secure SST acceptance among their customers by taking into account the moderating roles of

cultural dimensions and SST types. More specifically, a standardized global rollout of an SST

in culturally diverse service markets is likely to be problematic. Table 6, for example, reveals

that for service markets that are low in uncertainty avoidance, increasing provider- and

technology-related information reduces customers’ risk perceptions.

Finally, our moderation analysis results point to the importance of SST type for

designing effective rollout and subsequent management of SSTs (Table 6). For instance, to

counter the heightened negative influence of anxiety and ease of use for SST acceptance,

firms should invest serious effort in preventing embarrassment of public SST users (e.g.,

thoughtful location of self-service kiosks) and/or to increase SST ease of use.

LIMITATIONS AND FURTHER RESEARCH

The findings of our study can be effectively used to guide future research. First, we

recommend that SST studies combine different acceptance theories. Beaudry and

Pinsonneault (2010) propose complementing the more-cognitive acceptance models (e.g.,

TAM, UTAUT) with approaches that are more emotion-based, such as an appraisal tendency

framework. For instance, sad people are more likely to attribute failure of a new event to

situational factors rather than to themselves, and will adapt their behavior accordingly. Thus,

depending on the emotional state, different predictors may matter when considering SST

users’ emotions and thought processes.

30

Second, Venkatesh et al. (2012) argue that some factors are more important than

others for initial assessment of technologies. In a longitudinal study, future research should

examine whether the relevance of computer self-efficacy, perceptions of external control,

computer anxiety, and computer playfulness diminishes over time, and should investigate

whether perceived enjoyment gains importance with more hands-on experience.

Third, we find strong crossover effects between predictors, which is contrary to TAM.

For instance, we find that external control lowers usefulness perceptions. Customers may

conclude that the need for a company to provide additional support indicates that deficiencies

in the technology. Future research should investigate the types of SST customers for whom

this effect occurs, and the types of support for which the effect can be anticipated. With

regard to ease of use, we find that subjective norm and the need for interaction also impact

this mediator. To date, however, researchers have examined social processes only with regard

to usefulness perceptions. Future studies should expand understanding of the influence of a

reference group for perception of effort.

Fourth, we find that the importance of predictors depends on the type of SST. Future

research should extend study of the differences between public and private SSTs. Individuals

using SSTs in public may feel embarrassed if things go wrong but, conversely, public use

offers the opportunity to receive help from others. Future studies should expand knowledge

that helps to identify customers who are most likely to feel embarrassed, and those who will

seek support from other SST users.

Fifth, Hofstede’s dimensions may not capture all of the rich differences across

cultures, such as the degree to which a culture is horizontal or vertical. Triandis and Gelfand

(1998) propose that both individualism and collectivism may be horizontal (emphasizing

equality) or vertical (emphasizing hierarchy). For instance, they argue that American

individualism is different from Swedish individualism, depending upon the relative emphasis

31

on horizontal and vertical social relationships. Using their measurement in a cross-country

survey would further expand comprehension of SST use across cultures.

32

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TABLE 1

CONSTRUCT DEFINITION AND THEORETICAL ROOTS

Theoretical Roots

Construct Definition TAM UTAUT Other

Outcomes Usage behavior Actual system use in the context of technology acceptance (Davis et

al., 1989)

x x x(IDT)

Usage intention The strength of one's intention to perform a specified behavior (e.g.,

using an SST) (Davis et al., 1989) x x

Mediators Attitude toward using An individual’s positive or negative feelings (evaluative affect) about

performing the targeted behavior (e.g., using an SST) (Venkatesh et

al. 2003)

x

Usefulness The subjective probability that using a technology would improve the

way a user could complete a given task (Davis et al., 1989) x x

(performanc

e

expectancy)

x(IDT;

relative

advantage)

Ease of use The degree to which a user would find the use of a technology to be

free from effort (Davis et al., 1989)

x x(effort

expectancy) x(IDT;

complexity)

Determinants Subjective norm A person's perception that most people who are important to him/her

think he/she should or should not perform the behavior in question

(e.g., using an SST) (Venkatesh et al. 2003)

x x(social

influence)

External control The degree to which an individual believes that organizational and

technical resources exist to support the use of the system (Venkatesh

et al. 2003)

x x(facilitating

conditions)

Enjoyment The extent to which the activity of using a specific system is

perceived to be enjoyable in its own right, aside from any

performance consequences resulting from system use (Venkatesh

2000)

x x(hedonic

motivation)

Image The degree to which an individual perceives that use of an innovation

will enhance his or her status in his or her social system (Moore and

Benbasat 1991)

x x(IDT)

Result demonstrability The degree to which an individual believes that the results of using a

system are tangible, observable, and communicable (Moore and

Benbasat 1991)

x x(IDT;

observability

)

36

Self-efficacy The degree to which an individual believes that he or she has the

ability to perform a specific task/job using the computer (Venkatesh

2000)

x

Anxiety The degree of an individual’s apprehension, or even fear, when she/he

is faced with the possibility of using computers (Venkatesh 2000)

x

Computer playfulness The degree of cognitive spontaneity in microcomputer interactions

(Venkatesh 2000)

x

Habit The extent to which people tend to carry out behavior (e.g., using

SSTs) automatically because of learning (Venkatesh et al. 2012)

x

Age Customer age x

Gender Customer gender x

Experience A customer’s prior experience using technology in general (Meuter et

al. 2005) x

Compatibility The degree to which an innovation is perceived as being consistent

with existing values, needs, and experiences of potential adopters

(Moore and Benbasat 1991)

x(IDT)

Trialability The degree to which an innovation may be experimented with before

adoption (Moore and Benbasat 1991) x(IDT)

Risk Customer concerns about security, system failure, reliability, and

other personal, psychological, or financial risks associated with using

technology (Walker et al. 2002)

x(SST)

Technology readiness People’s propensity to embrace and use new technologies to

accomplish goals in home life and at work (Parasuraman and Colby

2015)

x(SST)

Need for interaction The desire to retain personal contact with others (particularly frontline

service employees) during a service encounter (Dabholkar 1996) x(SST)

Notes: SST=constructs outside TAM, UTAUT, and IDT, but relevant in SST research. Constructs in brackets are synonyms in respective theories.

37

FIGURE 1

SST ACCEPTANCE META-ANALYTIC FRAMEWORK

Notes: Superscripts (E/U/I/B) indicate that we hypothesize a relationship between the predictor and ease of use/usefulness/usage intention/usage behavior. We also hypothesize

moderating effects for usefulness and ease of use on other mediators and outcomes. For visual clarity, these effects are not incorporated in the figure.

Image U

Result demonstrability UIB

Self-efficacy EIB

Anxiety EIB

Computer playfulness E

Subjective norm UI

External control EIB

Enjoyment EI

Habit IB

Age B

Gender B

Experience EUB

Compatibility IB

Trialability IB

Risk IB

Technology readiness EUIB

Need for interaction UIB

TAM/UTAUT

TAM

UTAUT

Other Theories

Usefulness I

Ease of use UI

Outcomes

UsageIntention

Usage Behavior

Cultural Moderators• Power distance• Individualism• Masculinity• Uncertainty avoidance

Moderators

Controls• Research design• Sample composition

Technology Moderators• Purpose (transaction vs self-help)• Device (kiosk vs Internet)• Public SST (public vs private)• Hedonic SST (hedonic vs utilitarian)

Attitude Toward Using

Mediators

38

TABLE 2

DESCRIPTIVE STATISTICS ON THE OUTCOME-RELATED CORRELATIONS

95% CI

Theory Hyp. Relationship k N r rc rswc SE z-value Lower Upper FSN Chi2 Power

TAM/UTAUT Intention→Behavior 12 3150 .39 .43 .56 .11 5.057 .34 .77 3212 958* .999

Usefulness→Behavior 14 4826 .40 .43 .50 .07 7.453 .37 .64 5066 942* .999

Ease of use→Behavior 11 2976 .45 .48 .54 .07 7.280 .40 .69 3204 1132* .999

Subjective norm→Behavior 7 1351 .12 .15 .34 .12 2.763 .10 .58 170 121* .999

H1d External control→Behavior 8 1973 .01 .04 .28 .19 1.432 -.10 .66 — 755* .999

Enjoyment→Behavior 9 3525 .41 .43 .46 .07 6.364 .32 .61 2290 352* .999

All predictors→Behavior 61 6774 .32 .36 .47 .04 10.520 .38 .55 64850 4724* .999

TAM Image→Behavior — — — — — — — — — — — —

H2b Result demonstrability→Behavior — — — — — — — — — — — —

H2c Self-efficacy→Behavior 12 3539 .27 .30 .51 .11 4.866 .31 .72 2118 544* .999

H2d Anxiety→Behavior 7 1252 -.13 -.14 -.18 .04 4.612 -.26 -.10 86 14* .999

Computer playfulness→Behavior 3 427 .17 .21 .22 .03 7.008 .16 .28 16 1 .999

All predictors→Behavior 22 4570 .21 .24 .40 .07 5.587 .26 .54 3591 703* .999

UTAUT H3a Habit→Behavior 4 569 .37 .41 .30 .16 1.830 -.02 .62 — 98* .999

H3b Age→Behavior 5 3026 .06 .06 -.01 .05 .172 -.11 .09 — 40* .085

H3c Gender→Behavior 4 2822 .05 .05 .00 .09 .046 -.16 .17 — 89* .050

H3d Experience→Behavior 2 484 .17 .17 .16 .03 4.781 .10 .23 — 1 .943

All predictors→Behavior 15 3875 .15 .16 .04 .05 .831 -.06 .14 — 306* .702

Other theories Attitude→Behavior 3 777 .53 .59 .69 .18 3.748 .33 1,05 330 183* .999

H4a Compatibility→Behavior — — — — — — — — — — — — H4b Trialability→Behavior — — — — — — — — — — — — H4c Risk→Behavior 5 1237 .27 .29 .28 .06 4.365 .15 .40 200 28* .999

H4d Technology readiness→Behavior 2 1562 .72 .74 .73 .05 15.039 .64 .83 — 27* .999

H4e Need for interaction→Behavior 4 1095 -.07 -.08 -.14 .09 1.575 -.31 .03 — 35* .997

All predictors→Behavior 14 3814 .14 .15 .28 .12 2.259 .04 .53 1062 1517* .999

TAM/UTAUT H1a Usefulness→Intention 66 82256 .47 .52 .52 .03 17.512 .46 .57 142217 2676* .999

39

H1b Ease of use→Intention 55 72253 .29 .31 .43 .03 13.806 .37 .49 68476 4158* .999

H1c Subjective norm→Intention 29 67853 .29 .31 .25 .03 8.834 .19 .30 23061 6802* .999

H1d External control→Intention 15 61977 .23 .25 .21 .03 6.709 .15 .28 5930 846* .999

H1e Enjoyment→Intention 23 8294 .44 .50 .48 .04 13.255 .41 .56 16006 491* .999

All predictors→Intention 188 85396 .36 .40 .33 .02 20.981 .30 .37 989148 18946* .999

TAM Image→Intention — — — — — — — — — — — —

H2b Result demonstrability→Intention 1 781 .61 .62 .62 — — — — — — .999

H2c Self-efficacy→Intention 20 64308 .42 .45 .43 .03 15.490 .38 .48 31669 935* .999

H2d Anxiety→Intention 14 6176 -.27 -.28 -.22 .05 4.697 -.31 -.13 1850 260* .999

Computer playfulness→Intention 12 3202 .27 .31 .28 .08 3.775 .14 .43 1123 216* .999

All predictors→Intention 48 72980 .18 .20 .36 .03 10.607 .30 .43 32349 4015* .999

UTAUT H3a Habit→Intention 4 569 .35 .39 .37 .08 4.405 .20 .53 120 41* .999

Age→Intention 4 938 .03 .04 .08 .12 .732 -.14 .31 — 55* .689

Gender→Intention 2 539 .02 .02 .00 .03 .024 -.06 .07 — 1 .050

Experience→Intention 15 4931 .33 .36 .29 .07 4.341 .16 .42 3157 384* .999

All predictors→Intention 25 5860 .26 .29 .25 .05 4.938 .15 .36 4854 580* .999

Other theories Attitude→Intention 35 14624 .52 .56 .56 .06 10.063 .45 .67 52764 2638* .999

H4a Compatibility→Intention 5 59897 .62 .64 .65 .01 102.938 .63 .66 25115 50* .999

H4b Trialability→Intention 1 781 .58 .61 .61 — — — — — — .999

H4c Risk→Intention 20 64301 -.06 -.06 -.01 .03 .398 -.07 .05 — 911* .718

H4d Technology readiness→Intention 8 2652 .31 .33 .36 .07 5.415 .23 .49 965 117* .999

H4e Need for interaction→Intention 14 5584 -.18 -.20 -.17 .06 2.592 -.29 -.04 290 313* .999

All predictors→Intention 83 80237 .25 .26 .31 .04 7.976 .24 .39 151945 24151* .999

Notes: * p<.05-level. k=Number of observations; N=combined sample size over all independent samples; Min.=minimum; Max.=maximum; r=simple average correlation;

rc=average artifact-corrected correlation; rswc=sample-size weighted artifact-corrected correlation; FSN=Fail-safe N. FSNs were calculated when the main effect was

significant and when at least three observations were available.

40

FIGURE 2

TESTING RIVAL ACCEPTANCE MODELS

Usefulness

Ease of use

UsageIntention

Usage Behavior

Attitude Toward Using

Self-efficacy

Anxiety

Computer playfulness

Subjective norm

External control

Model 1: TAM

UsageIntention

Usage Behavior

Model 2: UTAUT

Subjective norm

External control

Experience

Usefulness

Ease of use

Mediators Outcomes

Outcomes

Model 3b: Revised Model

Usefulness

Ease of use

UsageIntention

Usage Behavior

Attitude Toward Using

OutcomesMediators

Self-efficacy

External control

Anxiety

Subjective norm

Computer playfulness

Experience

Need for interaction

Model 3a: Proposed Model

Usefulness

Ease of use

UsageIntention

Usage Behavior

Attitude Toward Using

OutcomesMediators

Self-efficacy

External control

Anxiety

Subjective norm

Computer playfulness

Experience

Need for interaction

41

TABLE 3

CORRELATIONS AMONG LATENT CONSTRUCTS

Construct 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 13.

1. Intention [.92]

2. Behavior .56 [.86]

3. Usefulness .52 .50 [.88]

4. Ease of use .43 .54 .62 [.88]

5. Subjective norm .25 .34 .36 .34 [.88]

6. External control .21 .28 .23 .06 -.09 [.88]

7. Anxiety -.22 -.18 -.23 -.16 .16 -.11 [.86]

8. Computer playfulness .28 .22 .23 -.01 .22 .57 -.18 [.84]

9. Experience .29 .16 .38 .06 -.04 .27 -.37 .35 [.88]

10. Attitude .56 .69 .60 .40 .13 .20 -.19 .30 .27 [.89]

11. Self-efficacy .43 .51 .58 .35 .23 .47 -.51 .50 .40 .36 [.89]

12. Need for interaction -.14 -.14 .13 -.02 .18 .04 .33 -.24 -.31 -.05 -.05 [.85]

SD 1.44 1.12 1.21 1.23 1.2 1.38 1.42 1.31 1.26 1.03 1.30 1.67

Notes: Harmonic mean across all collected effects is 1,730. Entries in the diagonal [ ] are weighted mean Cronbach’s alpha coefficients.

42

TABLE 4

RESULTS OF STRUCTURAL EQUATION MODEL

Model 1:

TAM

Model 2:

UTAUT

Model 3a:

Proposed

Model

Model 3b:

Revised

Model

Relationship B R2 B R2 B R2 B R2

Intention→Behavior .44* 31 .42* 35 .09* 65 .06* 75

Attitude→Behavior .58* .70*

Usefulness→Behavior -.18* -.24*

Ease of use→Behavior .27* .14*

Subjective norm→Behavior .31*

External control→Behavior (H1d) .15* .05* .24*

Self-efficacy→Behavior (H2c) .31* .33*

Anxiety→Behavior (H2d) .11* .07*

Computer playfulness→Behavior -.33*

Experience→Behavior (H3d) -.04* -.10* -.07*

Need for interaction→Behavior (H4e) -.09* -.17*

Attitude→Intention .75* 34 31 .50* 40 .52* 44

Usefulness→Intention (H1a) .42* .14* .10*

Ease of use→Intention (H1b) .21* .14* .15*

Subjective norm→Intention (H1c) .22* .09* .15* .17*

External control→Intention (H1d) .13* .07* .08*

Self-efficacy→Intention (H2c) .13* .14*

Anxiety→Intention (H2d) -.01 .00

Computer playfulness→Intention -.05

Experience→Intention .06*

Need for interaction→Intention (H4e) -.13* -.13*

Usefulness→Attitude .49* 35 .49* 36 .52* 42

Ease of use→Attitude .04* .04* .11*

Subjective norm→Attitude -.15*

External control→Attitude -.06*

Self-efficacy→Attitude -.08*

Anxiety→Attitude

Computer playfulness→Attitude .23*

Experience→Attitude -.02

Need for interaction→Attitude -.02

Ease of use→Usefulness (H1b) .55* 38 .54* 58 .48* 65

Subjective norm→Usefulness (H1c) .17* .15* .08*

External control→Usefulness -.05*

Self-efficacy→Usefulness .26*

Anxiety→Usefulness .02

Computer playfulness→Usefulness .05*

Experience→Usefulness (H3d) .41* .31*

Need for interaction→Usefulness (H4e) .18* .18*

Subjective norm→Ease of use 17 17 .41* 29

External control→Ease of use (H1d) -.04 -.04 .16*

Self-efficacy→Ease of use (H2c) .49* .49* .35*

Anxiety→Ease of use (H2d) .05* .04* -.04*

Computer playfulness→Ease of use (H2e) -.22* -.21* -.39*

Experience→Ease of use (H3d) -.04* -.05*

Need for interaction→Ease of use -.13*

Chi2(df) 2,555(24) 443(4) 1,199(17) .23(1)

CFI .77 .90 .91 1.00

AGFI .60 .57 .60 1.00

RMSEA .25 .25 .20 .00

SRMR .17 .08 .07 .00

* p<.05 (one-tailed). a. Anxiety was excluded because it was insignificant and the model would be saturated.

43

TABLE 5

INLFUENCE OF MODERATORS ON PREDICTORS’ EFFECTIVENESS

Usefuln. a

Ease

of use

Subjective

norm

External

control

Self-

efficacy Anxietyb

Computer

playfulness Experience Riskb

Need for

interactionb Enjoyment Compatibility

Moderator

Power distance -.001 .002 -.001 .020* -.008** .016 .009* -.008 .002 .021** .015** -.038

Individualism -.001 .000 -.002 .007 -.005** .009* .001 -.009 -.002 .013** .004** -.016*

Masculinity .001 .002 .014** .006 .006** -.003 .004 .007* .015** -.007* -.009** -.027

Uncertainty avoidance -.003** -.003** .005** -.002 -.005** .001 .007** -.006 .001 .016** -.001 -.018

Trans. (self-help) -.073* -.298** .043 -.311 -.071 .155 .123 .294 .056 -.588** -.005 —

Kiosk (Internet) -.087* .008 -.322** .178 .019 -.313 .187* .402** .161* -.210 .071 —

Public (private) .046 .222** -.091 .003 .056 -.039 -.003 .461** -.363** -.122 -.051 .385

Hedonic (utilitarian) -.026 -.271** -.396** .509* -.455** -.052 -.029 -.255* -.119 -.013 .119** —

Intention -.105** -.150** .170** -.009 -.083 .055 .196** .194** -.221** -.138** .253** .189

Behavior -.251** -.070 .005 -.229** -.100 .194* -.057 .062 -.043 .041 .190** —

Attitude — -.095** .045 -.073 -.153** .009 .103 .152 -.141 -.012 — —

Usefulness — — .133** -.012 -.027 .032 .170** .236** -.274** .028 .217** .271*

Research design -.066 .134** .266** -.321 .155 .131 — -.275* .141** -.158 .058 —

Sample composition -.054 -.071 .097 .167 .100* -.014 .046 .264* .051 .135 -.087 — ** p<.05 (one-tailed); * p<.1 (one-tailed).

a. The coefficients in this table can be read as follows. The reliability-corrected correlations of usefulness are weaker for countries high in uncertainty avoidance (-.003), for

transaction SSTs than for self-help SSTs (-.073), for Kiosk than for Internet (-.087), for the usefulness-intention correlation (-.105), and for usefulness-behavior correlations

(-.251).

b. The main effects of need for interaction, anxiety, and risk are negative, which must be considered when interpreting the moderating effects.

c. Because the predictor-outcome moderators were dummy coded and can be presented as linear combinations of each other, one moderator must be excluded.

Notes. The table reports unstandardized coefficients. While cultural dimensions were measured on Hofstede’s (2001) cultural dimensions using an index ranging from 0-100,

the other variables were measured with dummy variables of either 1 or 0. A dash indicates that a moderator could not be tested.

1

TABLE 6: SUMMARY OF MODERATING EFFECTS

Hyp. Prediction Finding Plausible Explanation for Nonsupport

Power Distance

H5a Effect of external control is stronger in high power distance

cultures.

Supported

H5b Effect of need for interaction is stronger in high power distance

cultures.

Rejected(‒) Need for interaction is weaker in high power distance cultures

because firms are less considerate of customers (Hofstede 2001).

H5c Effect of self-efficacy is stronger in low power distance cultures. Supported

H5d Effect of experience is stronger in low power distance cultures. Rejected(ns) Company support in collectivistic cultures can only partially

substitute lacking individual experience.

Individualism–Collectivism

H6a Effect of enjoyment is stronger in individualistic cultures. Supported

H6b Effect of subjective norm is stronger in collectivistic cultures. Rejected(ns) The group is used in individualistic cultures to display individual

status, while in collectivistic cultures, membership is key.

H6c Effect of self-efficacy is stronger in collectivistic cultures. Supported

H6d Effect of anxiety is stronger in collectivistic cultures. Supported

H6e Effect of need for interaction is stronger in collectivistic cultures. Supported

Masculinity–Femininity

H7a Effect of self-efficacy is stronger in masculine cultures. Supported

H7b Effect of experience is stronger in masculine cultures. Supported

H7c Effect of subjective norm is stronger in feminine cultures. Rejected(‒) Subjective norms are stronger in masculine cultures. Because

masculine cultures are functionality oriented, and SST is

functional, norms are more important in masculine cultures.

H7d Effect of need for interaction is stronger in feminine cultures. Rejected(‒) Need for interaction is stronger in masculine cultures. Because

masculine cultures are functionality oriented, interactions with

others about SST functions are more important.

H7e Effect of enjoyment is stronger in feminine cultures. Supported

H7f Effect of risk is stronger in feminine cultures. Supported

Uncertainty Avoidance

H8a Effect of subjective norm is stronger in high uncertainty avoidance

cultures.

Supported

H8b Effect of experience is stronger in high uncertainty avoidance

cultures. Rejected(ns) In high uncertainty avoidance cultures, customers prefer external

instead of internal cues (experience) to reduce uncertainty.

2

H8c Effect of computer playfulness is stronger in high uncertainty

avoidance cultures. Supported

H8d Effect of usefulness is stronger in low uncertainty avoidance

cultures. Supported

H8e Effect of ease of use is stronger in low uncertainty avoidance

cultures. Supported

H8f Effect of need for interaction is stronger in low uncertainty

avoidance cultures. Supported

Transaction/Self-help SSTs

H9a Effect of experience is stronger for transaction SSTs. Rejected(ns) Self-help SSTs also require experience because technology

generally requires some understanding by the customer.

H9b Effect of risk is stronger for transaction SSTs. Rejected(ns) Self-help SSTs also have risk because failures may also occur when

customers use these SSTs and experience poor services.

H9c Effect of need for interaction is stronger for transaction SSTs. Supported

H9d Effect of usefulness is stronger for self-help SSTs. Supported

H9e Effect of ease of use is stronger for self-help SSTs. Supported

H9f Effect of external control is stronger for self-help SSTs. Rejected(ns) Due to the financial risk of transaction SSTs, external control also

matters for these SSTs.

Kiosk/Internet SSTs

H10a Effect of subjective norm is stronger for Internet SSTs. Supported

H10b Effect of risk is stronger for Internet SSTs. Supported

H10c Effect of computer playfulness is stronger for kiosk SSTs. Supported

Public/Private SSTs

H11a Effect of ease of use is stronger for public SSTs. Supported

H11b Effect of anxiety is stronger for public SSTs. Rejected(ns) When using public SSTs, customers may receive emotional support

from other individuals.

H11c Effect of experience is stronger for public SSTs. Supported

H11d Effect of risk is stronger for public SSTs. Supported

Hedonic/Utilitarian SSTs

H12a Effect of computer playfulness is stronger for hedonic SSTs. Rejected(ns) Computer playfulness refers to general perceptions about

technology use, while hedonic benefits are technology-specific.

H12b Effect of enjoyment is stronger for hedonic SSTs. Supported

H12c Effect of usefulness is stronger for utilitarian SSTs. Rejected(ns) A satisfying use experience is as important for utilitarian SSTs as it

is for hedonic SSTs.

H12d Effect of ease of use is stronger for utilitarian SSTs. Supported

3

H12e Effect of subjective norm is stronger for utilitarian SSTs. Supported

WEB APPENDIX A

TABLE A

PRIOR RESEARCH SUPPORTING THE DIRECT EFFECTS OF THE DETERMINANTS ON SST ACCEPTANCE

DV: Usage Intention DV: Usage Behavior

IVs

Theoretical Justification Empirical Support Theoretical Justification Empirical Support

Subjective norm TAM and UTAUT. To gain social

acceptance/approval, consumers will

be more willing to use an SST if

people important to them think they

should use the technology.

Chen et al. (2009)

Chiu and Hofer (2015)

Curran and Meuter (2007)

Jia et al. (2012)

Mortimer et al. (2015)

/ /

External control UTAUT. A consumer who has access

to a favorable set of facilitating

conditions is more likely to have a

higher intention to use a technology.

Chen et al. (2009)

Chiu and Hofer (2015)

Dabholkar (1996)

Jia et al. (2012)

Lee and Allaway (2002)

UTAUT. Facilitating conditions serve

as a proxy for actual behavioral

control, and they influence behavior

directly.

/

Enjoyment UTAUT. All else being equal,

consumers are more willing to use an

SST that is enjoyable and fun to use

(high intrinsic/hedonic motivation).

Curran and Meuter (2007)

Dabholkar (1996)

Dabholkar and Bagozzi (2002)

/ /

Image

/ / / /

Result demonstrability Result demonstrability will increase

motivation to use an SST by enabling

users to observe the outcome of using

it.

Eastlick (1996) IDT. Seeing how others use an SST

and observing a good outcome from

using it will encourage customers to

adopt the technology.

Lee et al. (2003)

Meuter et al. (2005)

Self-efficacy Social cognition theory. Consumers

are more willing to use an SST if they

Hsiao and Tang (2015)

van Beuningen et al. (2009)

Wang et al. (2013)

When consumers believe that they

lack the skills and confidence to use

Meuter et al. (2005)

Walker and Johnson (2006)

4

DV: Usage Intention DV: Usage Behavior

IVs

Theoretical Justification Empirical Support Theoretical Justification Empirical Support

believe they can use it properly and

they achieve their desired outcomes.

Zhao et al. (2008)

an SST, they will not use it, even if it

is a better alternative.

Anxiety Consumers with higher levels of

technology anxiety are more negative

concerning confidence in their own

ability to address technology and their

willingness to use it.

Gelbrich and Sattler (2014)

Jia et al. (2012)

Zhao et al. (2008)

High levels of technology anxiety

may lead to avoidance of using

technology, including SSTs.

Evanschitzky et al. (2015)

Meuter et al. (2003)

Meuter et al. (2005)

Computer playfulness

/ / / /

Habit UTAUT. A strong habit of using an

SST can result in a well-established

usage intention stored in the

conscious mind of a consumer.

/ UTAUT. Consumers may use an SST

by habit, without intentional cognitive

thinking.

Jolley et al. (2006)

Limayem and Hirt (2003)

Verplanken (2006)

Wang et al. (2013)

Age / / UTAUT. Younger consumers are

more likely to adopt SSTs because

they are more receptive to

innovations.

Lee et al. (2003)

Meuter et al. (2003)

Meuter et al. (2005)

Nilsson (2007)

Gender / / UTAUT. Men are more likely to

adopt SSTs because they use

technology more frequently and are

generally more interested in

technology.

Meuter et al. (2003)

Meuter et al. (2005)

Nilsson (2007)

Experience / / UTAUT. Heavy users of related

technologies are more likely to try

SSTs because they are more confident

in using technology.

McKechnie et al. (2006)

Meuter et al. (2005)

5

DV: Usage Intention DV: Usage Behavior

IVs

Theoretical Justification Empirical Support Theoretical Justification Empirical Support

Compatibility Compatibility will increase

motivation because the SST will be

consistent with values and lifestyle.

Eastlick (1996)

Eastlick et al. (2012)

IDT. Increased compatibility with

personal values/lifestyle increases the

odds of a consumer trying the SST.

Meuter et al. (2005)

Moore and Benbasat (1991)

Trialability Trialability will increase motivation

by enabling users to observe how the

SST works.

Eastlick (1996) IDT. An SST that can be tested under

the consumer’s own conditions (high

trialability) is more likely to be

adopted.

Lee et al. (2003)

Meuter et al. (2005)

Risk As perceived risk increases, the

likelihood of benefits decreases,

reducing the motivation to use an

SST.

Curran and Meuter (2005)

Lee and Allaway (2002)

Kim and Qu (2014)

Mortimer et al. (2015)

Walker et al. (2002)

When consumers perceive an SST

that is highly likely to go wrong (high

risk), they will avoid using it and turn

to traditional personal service.

Meuter et al. (2005)

Walker and Johnson (2006)

Technology readiness Consumers with a high level of

technology readiness are highly

motivated to use SSTs because they

are generally interested and confident

in trying new technologies.

Chen et al. (2009)

Elliott et al. (2008)

Lin and Chang (2011)

Lin and Hsieh (2006)

Lin et al. (2007)

SST users generally have a

technology readiness score that is

significantly higher than that of

nonusers.

Parasuraman and Colby

(2015)

Need for interaction Greater desire for interpersonal

interaction may decrease consumer

willingness to use SSTs because these

consumers value personal contact and

tend to avoid machines.

Curran and Meuter (2005)

Hsiao and Tang (2015)

Kaushik and Rahman (2015)

Oh et al. (2013)

The desire for personal contact

increases the likelihood of using

personal service and decreases the

likelihood of using an SST during a

service encounter.

Evanschitzky et al. (2015)

Gelderman et al. (2011)

Lee et al. (2003)

Meuter et al. (2005)

Walker and Johnson (2006)

6

TABLE B

PRIOR RESEARCH SUPPORTING THE INDIRECT EFFECTS OF THE DETERMINANTS ON SST ACCEPTANCE

DV: Usefulness DV: Ease of Use

IVs

Theoretical Justification Empirical Support Theoretical Justification Empirical Support

Subjective norm TAM. If people important to a

consumer suggest that an SST might

be useful, the consumer may come to

believe that it is useful.

Chen et al. (2009)

Venkatesh and Bala (2008)

Venkatesh and Davis (2000)

/ /

External control / / TAM. Consumers may find an SST

easy to use when they have access to

a favorable set of technology and

resource-facilitating conditions (e.g.,

availability of clear instructions).

Chen et al. (2009)

Venkatesh (2000)

Venkatesh and Bala (2008)

Enjoyment / / TAM. Lack of enjoyment in use of an

SST may be perceived as requiring

more effort, and that the SST is more

difficult to use.

Venkatesh (1999)

Venkatesh (2000)

Venkatesh and Bala (2008)

Image TAM. The belief that using an SST

enhances a consumer’s status in his or

her social group may lead to a high

perceived usefulness of the SST,

beyond any tangible benefits.

Venkatesh and Bala (2008)

Venkatesh and Davis (2000)

/ /

Result demonstrability TAM. If the outcome of using an SST

is tangible and discernable, the

consumer may better understand how

to use it to produce desired results,

thus forming a more positive

perception of SST usefulness.

Venkatesh and Bala (2008)

Venkatesh and Davis (2000)

/ /

7

DV: Usefulness DV: Ease of Use

IVs

Theoretical Justification Empirical Support Theoretical Justification Empirical Support

Self-efficacy / / TAM. A consumer’s confidence in

his or her abilities and skills for using

an SST can serve as the basis for the

consumer’s judgment about the ease

or difficulty of SST use.

Gelbrich and Sattler (2014)

Venkatesh (2000)

Venkatesh and Bala (2008)

Venkatesh and Davis (1996)

Anxiety / / TAM. Consumers with higher levels

of technology anxiety tend to perceive

the use of SSTs to be more difficult.

Gelbrich and Sattler (2014)

Venkatesh (2000)

Venkatesh and Bala (2008)

Computer playfulness / / TAM. Consumers who are more

playful with computer technologies in

general may tend to underestimate the

difficulty of using an SST because

they enjoy using it and do not

perceive it as being effortful.

Venkatesh (2000)

Venkatesh and Bala (2008)

Habit

/ / / /

Age

/ / / /

Gender

/ / / /

Experience

Consumers who are more experienced

with an SST may better understand

how to gain greater advantage from it,

which results in greater awareness of

its potential usefulness.

Gefen et al. (2003)

Karahanna et al. (1999)

Consumers who are more experienced

with an SST may better understand

how to properly operate it, which

results in increased perception that the

SST is easy to use.

Gefen et al. (2003)

Karahanna et al. (1999)

Compatibility

/ / / /

8

DV: Usefulness DV: Ease of Use

IVs

Theoretical Justification Empirical Support Theoretical Justification Empirical Support

Trialability

/ / / /

Risk

/ / / /

Technology readiness Consumers with a high level of

technology readiness may find an

SST useful because they are

interested in using it and are

motivated to explore its benefits.

Chen et al. (2009)

Elliott et al. (2012)

Lin and Chang (2011)

Lin et al. (2007)

Consumers with a high level of

technology readiness may find an

SST easy to use because they are

confident in using technology and

tend to overestimate their ability to

use it.

Chen et al. (2009)

Elliott et al. (2012)

Lin and Chang (2011)

Lin et al. (2007)

Need for interaction Consumers with a greater desire for

personal contact will find fewer

benefits of using an SST because

there is no interpersonal interaction in

a typical SST service encounter.

Oh et al. (2013) / /

9

TABLE C

DESCRIPTIVE STATISTICS ON THE MEDIATOR-RELATED CORRELATIONS

95% CI

Theory Hyp. Relationship k N r rc rswc SE z-value Lower Upper FSN Chi2 Power

TAM/UTAUT Usefulness→Attitude 37 14226 .53 .59 .60 .03 19.105 .53 .66 64687 954* .999

Ease of use→Attitude 33 11056 .39 .44 .40 .04 10.167 .33 .48 24211 1035* .999

Subjective norm→Attitude 7 1974 .12 .12 .13 .09 1.376 -.05 .31 108 121* .999

External control→Attitude 1 145 .17 .20 .20 — — — — — — .679

Enjoyment→Attitude 12 5445 .43 .48 .50 .06 8.274 .38 .62 5247 369* .999

TAM Image→Attitude — — — — — — — — — — — —

Result demonstrability→Attitude — — — — — — — — — — — —

Self-efficacy→Attitude 7 2272 .35 .39 .36 .10 3.792 .18 .55 826 184* .999

Anxiety→Attitude 3 1591 -.29 -.32 -.19 .09 2.241 -.36 -.02 92 31* .999

Computer playfulness→Attitude 4 2441 .27 .31 .30 .05 6.395 .21 .40 311 23* .999

UTAUT Habit→Attitude —

Age→Attitude 1 204 .15 .15 .15 — — — — — — .574

Gender→Attitude — — — — — — — — — — — —

Experience→Attitude 5 2405 .36 .40 .27 .08 3.460 .12 .43 466 107* .999

Other theories Compatibility→Attitude 2 1415 .52 .60 .58 .02 34.378 .54 .61 — 24* .999

Trialability→Attitude — — — — — — — — — — — —

Risk→Attitude 8 2816 .24 .29 .25 .09 2.746 .07 .42 573 208* .999

Technology readiness→Attitude 4 1826 .30 .30 .33 .03 9.585 .26 .39 275 13* .999

Need for interaction→Attitude 10 3033 -.04 -.05 -.05 .09 .530 -.22 .13 8 239* .787

TAM/UTAUT H1b Ease of use→Usefulness 62 77897 .43 .47 .62 .03 19.740 .55 .68 190860 6105* .999

H1c Subjective norm→Usefulness 25 65788 .28 .30 .36 .03 11.960 .30 .42 28453 4536* .999

External control→Usefulness 11 61319 .33 .35 .23 .02 11.686 .19 .27 6381 259* .999

Enjoyment→Usefulness 27 9975 .40 .44 .47 .04 13.362 .40 .54 19773 630* .999

TAM H2a Image→Usefulness 1 152 .12 .12 .12 — — — — — — .314

H2b Result demonstrability→Usefulness 1 828 .56 .60 .60 — — — — — — .999

Self-efficacy→Usefulness 20 64547 .40 .45 .58 .02 32.405 .55 .62 42708 585* .999

Anxiety→Usefulness 9 4681 -.18 -.22 -.23 .04 5.435 -.31 -.15 546 54* .999

Computer playfulness→Usefulness 7 1620 .24 .29 .23 .06 3.578 .10 .35 241 39* .999

10

UTAUT Habit→Usefulness 2 145 .24 .31 .31 .04 8.144 .24 .39 — 0 .969

Age→Usefulness 2 539 -.06 -.06 .04 .19 .234 -.32 .41 — 38* .153

Gender→Usefulness 2 539 -.02 -.02 -.04 .04 1.112 -.12 .03 — 1 .153

H3d Experience→Usefulness 10 3157 .33 .39 .38 .05 7.038 .27 .48 1542 114* .999

Other theories Compatibility→Usefulness 6 60096 .56 .58 .72 .02 43.791 .68 .75 30642 129* .999

Trialability→Usefulness 1 828 .54 .64 .64 — — — — — — .999

Risk→Usefulness 22 65185 -.08 -.09 -.08 .03 -2.468 -.15 -.02 1047 1286* .999

H4d Technology readiness→Usefulness 3 2009 .29 .31 .35 .07 4.913 .21 .49 233 36* .999

H4e Need for interaction→Usefulness 11 4520 -.01 -.03 .13 .14 .926 -.14 .40 20 987* .999

TAM/UTAUT Subjective norm→Ease of use 21 64908 .18 .21 .34 .02 19.099 .31 .38 12535 420* .999

H1d External control→Ease of use 10 60755 .33 .35 .06 .05 1.255 -.04 .16 1584 1441* .999

H1e Enjoyment→Ease of use 21 5868 .20 .22 .20 .09 2.292 .03 .37 2367 1150* .999

TAM Image→Ease of use — — — — — — — — — — — —

Result demonstrability→Ease of use 1 828 -.53 -.61 -.61 — — — — — — .999

H2c Self-efficacy→Ease of use 24 65094 .37 .42 .35 .09 3.789 .17 .54 7834 1248* .999

H2d Anxiety→Ease of use 10 2650 -.30 -.34 -.16 .13 1.184 -.41 .10 648 589* .999

H2e Computer playfulness→Ease of use 8 1933 .06 .08 -.01 .10 .135 -.20 .17 -4 95* .072

UTAUT Habit→Ease of use 2 145 .28 .36 .35 .07 5.055 .21 .49 — 1 .992

Age→Ease of use 2 482 -.26 -.27 -.23 .11 2.070 -.44 -.01 — 13* .999

Gender→Ease of use 1 179 .14 .14 .14 — — — — — — .842

H3d Experience→Ease of use 9 2436 .22 .23 .06 .16 .379 -.25 .37 293 507* .999

Other theories Compatibility→Ease of use 6 60096 .29 .30 .73 .08 8.836 .57 .89 23855 3724* .999

Trialability→Ease of use 1 828 -.48 -.59 -.59 — — — — — — .999

Risk→Ease of use 23 65682 .15 .18 -.02 .04 .403 -.09 .06 1767 1707* .999

H4d Technology readiness→Ease of use 3 2009 .11 .11 .00 .29 .017 -.57 .56 5 493* .050

Need for interaction→Ease of use 11 4741 -.04 -.05 -.02 .09 .198 -.20 .16 -5 373* .280

Notes: * p<.05-level. k=Number of observations; N=combined sample size over all independent samples; Min.=minimum; Max.=maximum; r=simple average correlation;

rc=average artifact-corrected correlation; rswc=sample-size weighted artifact-corrected correlation; FSN=Fail-safe N. FSNs were calculated when the main effect was

significant and when at least three observations were available.

11

TABLE D

DIRECT, INDIRECT, AND TOTAL EFFECTS

Behavior Intention Attitude Usefulness Ease of use

Determinants D I T % D I T % D I T % D I T % D I T %

Intention .06 — .06 —

Attitude .70 .03 .73 4% .52 — .52 —

Usefulness -.24 .38 .15 72% .10 .27 .37 42% .52 — .52 —

Ease of use .14 .15 .30 34% .15 .23 .38 38% .11 .25 .35 41% .48 — .48 —

Subjective norm .31 .03 .34 9% .17 .11 .28 27% -.15 .19 .04 84% .08 .20 .28 42% .41 — .41 — External control .24 .00a .25 2% .08 .01a .10 12% -.06 .03 -.02a 57% -.05 .08 .03a 71% .16 — .16 — Self-efficacy .33 .09 .42 18% .14 .19 .33 37% -.08 .26 .18 59% .26 .17 .43 28% .35 — .35 — Anxiety .07 -.01a .06 15% .00a -.01a -.01a 50% — -.01a -.01a 50% .02a -.02 .00a 88% -.04 — -.04 — Computer playfulness -.33 .06 -.27 17% -.05a -.01 -.06 14% .23 -.11 .12 48% .05 -.19 -.14 58% -.39 — -.39 — Experience -.07 .02a -.06 23% .06 .08 .15 37% -.02 .14 .12 55% .31 -.02 .29 7% -.05 — -.05 — Need for interaction -.17 -.04 -.21 15% -.13 .01a -.12 5% -.02a .05 .02a 66% .18 -.06 .12 35% -.13 — -.13 — Average 21% 29% 58% 47%

a. Not significant (p>.05; one-tailed); all other effects are significant at p<.05.

D=direct effect; I=indirect effect; T=total effect; %=relative importance of indirect effects.

12

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15

WEB APPENDIX B

[1] Data Collection and Coding

When collecting the studies, we had to decide which SST studies to include or exclude. We

imposed three criteria for inclusion: (1) it is a service; (2) no service employee is involved;

and (3) the customer produces a service through the technology rather than simply receiving.

For 114 studies, country information could be gathered from 24 countries: Australia, Canada,

China, Estonia, Finland, Germany, Greece, India, Indonesia, Italy, South Korea, Malaysia,

Netherlands, Norway, Singapore, Slovenia, Spain, Sweden, Taiwan, Thailand, Turkey, UK,

USA, and Vietnam. Additionally, 116 studies report information on SST purpose

(transaction: 63, self-help: 53), and 107 report information on SST device (kiosk: 46,

Internet: 61). We identified a total of 108 studies as demonstrating SST publicity (public: 57,

private: 51), and 117 studies as SST hedonism (hedonic: 20, utilitarian: 97). Furthermore, 116

studies report information on research design (experiment: 21, survey: 95) and sample

composition (student: 37, non-student: 79).

[2] Integration of Effect Sizes

When integrating effect sizes, we did not use Fisher z-transformation because the method

overestimates true effect sizes by 15% – 45% (Field 2001). We corrected effect sizes for

several artifacts, including dichotomization and range restriction. The artifact correction for

dichotomization is: adichot.=Φ(c) / (P * Q)1/2, with P and Q=proportions in both groups and

Φ(c)=normal ordinate at point c. The artifact correction for range restriction is arange=(u2 + r2

(1 ‒ u2))1/2, with u=SDstudy / SDreference. As a reference SD, we used the SD of studies that

were unaffected by range restriction (Hunter and Schmidt 2004). We also tested the statistical

power for effect size integration. Statistical power refers to the probability of not rejecting a

false null hypothesis (Type II error, defined by ). Therefore, we define power as (1 – ),

interpreted as the probability that a statistical test will correctly reject a false null hypothesis.

We identified meta-analyses as having sufficient power to detect meaningful effect sizes, if

the power values are larger than .5 (Muncer et al. 2003).

[3] Evaluation of Causal Model

We used a harmonic mean for calculating the SEM because it is lower than the arithmetic

mean, and the estimations in the SEM are more conservative (Viswesvaran and Ones 1995).

Because we measured all constructs with a single indicator and because we had already

considered measurement errors when calculating the mean effect sizes, we set error variances

in the SEM to zero.

[4] Moderator Analysis

The need for moderator analysis is assessed in different ways. First, we reviewed all

examined studies and counted how often a predictor was reported to significantly or

insignificantly relate to a mediator or outcome variable. We report the % of predictors with a

non-significant influence for subjective norm (26%), external control (31%), enjoyment

(8%), image (0%), result demonstrability (67%), self-efficacy (18%), anxiety (19%),

computer playfulness (17%), habit (25%), age (36%), gender (67%), experience (11%),

compatibility (14%), trialability (33%), risk (30%), technology readiness (11%), need for

interaction (53%), usefulness (5%), and ease of use (26%). Second, we used the Chi2 test of

homogeneity and the 75% rule of thumb (Hunter and Schmidt 2004). The 75% rule of thumb

indicates the proportion of variance in the distribution of effect sizes that can be attributed to

sampling error. Cases with proportions lower than 75% suggest moderator analysis. We

coded the moderators in our multivariate, multilevel meta-regression as follows. In total, we

16

examine 24 countries, with cultural dimensions varying from 31 to 100 for power distance,

14 to 91 for individualism, 5 to 70 for masculinity-femininity, and 8 to 100 for uncertainty

avoidance. SST type is included, using four additional dummy variables: SST device

(1=kiosk, 0=Internet), SST purpose (1=transaction, 0=self-help), hedonistic SST

(1=hedonistic, 0=utilitarian), and public SST (1=public, 0=private). The methodological

moderators in the model include research design (1=experiment, 0=survey) and sample

composition (1=student, 0=non-student). The dependent variable correlating with predictors

is also coded as a dummy variable including intention (1=intention, 0=non-intention),

behavior (1=behavior, 0=non-behavior), attitude (1=attitude, 0=non-attitude), usefulness

(1=usefulness, 0=non-usefulness), and ease of use (1=ease of use, 0=non-ease of use).

[5] Testing Publication Bias

All significant relationships were found to be robust against publication bias, except for the

computer playfulness-behavior link, which shows an FSN of 16, which is below 25. The

average file-drawer N across all individual relationships is 16,227, and the tolerance criteria

suggested by Rosenthal (1979) are fulfilled. Rosenthal suggests tolerance levels and indicates

that FSN should be greater than 5 x k + 10, with k=number of correlations. The Chi2 test of

homogeneity is significant in most cases, and the data are characterized by heterogeneity.

Additionally, in most cases, the 75% rule of thumb suggests moderator analysis.

[6] Results of Power Tests

Testing the power of our tests indicates that nearly all of them have a sufficient power

exceeding the .5 level (Ellis 2010). We calculated the reported power tests using cumulative

sample size N. We also tested power with a more conservative test using N/k as the sample

size and found nearly identical results. We tested the stability of the results by excluding

larger sample sizes from the analyses, but the descriptive statistics remain the same.

[7] Results of Moderator Analysis

In additional analyses, we also tested whether the outlet of publication affects the results. We

split the publication outlet into two groups, in accordance with Academic Journal Guide

2015. The inclusion of this dummy variable did not affect the findings.

[8] References

Academic Journal Guide (2015), “Academic Journal Guide 2015,” Chartered Association of

Business Schools (accessed March 5, 2016), [available at www.charteredabs.org].

Ellis, Paul D. (2010), The Essential Guide to Effect Sizes: An Introduction to Statistical

Power, Meta-Analysis and the Interpretation of Research Results. Cambridge, UK:

Cambridge University Press.

Field, A. P. (2001), “Meta-Analysis of Correlation Coefficients: A Monte Carlo Comparison

of Fixed- and Random-Effects Methods,” Psychological Methods, 6 (2), 161-180.

Hunter, John E., and Frank L. Schmidt (2004), Methods of Meta-Analysis: Correcting Error

and Bias in Research Findings. Thousand Oaks, CA: Sage Publications.

Muncer, Steven J., Mark Craigie, and Joni Holmes (2003), “Meta-Analysis and Power: Some

Suggestions for the Use of Power in Research Synthesis,” Understanding Statistics:

Statistical Issues in Psychology, Education, and the Social Sciences, 2 (1), 1-12.

Rosenthal, Robert (1979), “The ‘File-Drawer Problem’ and Tolerance for Null Results,”

Psychological Bulletin, 86 (3), 638-41.

Viswesvaran, Chockalingam, and Deniz S. Ones (1995), “Theory Testing: Combining

Psychometric Meta-Analysis and Structural Equations Modeling,” Personnel

Psychology, 48 (4), 865-85.

17

WEB APPENDIX C

STUDIES INCLUDED IN META-ANALYSIS

Agudo-Peregrina, Á. F., Á. Hernández-García, and F. J. Pascual-Miguel (2014), “Behavioral

Intention, Use Behavior and the Acceptance of Electronic Learning Systems:

Differences between Higher Education and Lifelong Learning,” Computers in Human

Behavior, 34, 301-314.

Anıtsal, İ. (2005). “Technology-Based Self-Service: From Customer Productivity toward

Customer Value,” doctoral dissertation, University of Tennessee, Knoxville.

Ariff, M. S. M., S. M. Yeow, N. Zakuan, and A. Z. Bahari (2013), “Acceptance of Internet

Banking Systems among Young Managers,” IOP Conference Series: Materials Science

and Engineering, 46(1), 1-6.

Azmi, A. C., and N. L. Bee (2010), “The Acceptance of the e-Filing System by Malaysian

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18

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