The role of context and motivation variables in mobile commerce ...

Technological Forecasting & Social Change 88 (2014) 156–161

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Technological Forecasting & Social Change

The role of context and motivation variables in mobile commerce usage — A further perspective on Chong (2013)☆ Volker G. Kuppelwieser a,⁎, Marko Sarstedt b,c, Sven Tuzovic d a b c d

NEOMA Business School, 1 rue du Marechal Juin, 76825 Mont-Saint-Aignan, France Otto-von-Guericke-University Magdeburg, Universitätsplatz 2, 39106 Magdeburg, Germany University of Newcastle, Faculty of Business and Law, Newcastle, Australia Pacific Lutheran University, School of Business, Tacoma, WA 98447, USA

a r t i c l e

i n f o

Article history: Received 6 October 2013 Received in revised form 14 June 2014 Accepted 30 June 2014 Available online xxxx Keywords: M-commerce Demographic variable Consumer behavior Future time perspective Value Floor effects Ceiling effects

a b s t r a c t We comment on a recent article by Chong (2013) on the roles of demographic and motivation variables in mobile commerce usage. Drawing on the recent research on the service-dominant logic, socioemotional selectivity theory, and data from a first empirical study, we argue that a broader discussion on the value relevance of mobile commerce activities for customers and the consideration of consumers' future time perspectives would provide a richer, potentially more appropriate picture of the drivers of mobile commerce usage. Furthermore, using data from a second empirical study, we highlight several validity issues of the used scales. We hope to motivate a replication and extension of Chong's study and also provide recommendations for future research on this area. © 2014 Elsevier Inc. All rights reserved.

1. Introduction In his insightful and important article, Chong (2013) shows how demographic and motivational variables influence the adoption of a new technology, in this case, the use of mobile commerce (m-commerce). Focusing on the Chinese market, Chong (2013) proposes a new model of m-commerce usage activities instead of m-commerce adoption, and concludes that younger users usually engage in these activities. Although we agree with the general substance of his text, the article may cause researchers and practitioners to misinterpret the true value of the results for the following reasons:

☆ The authors would like to thank Armin Monecke (Ludwig-MaximiliansUniversity, Munich, Germany) for his support with the analyses. ⁎ Corresponding author. E-mail addresses: [email protected] (V.G. Kuppelwieser), [email protected] (M. Sarstedt), [email protected] (S. Tuzovic).

http://dx.doi.org/10.1016/j.techfore.2014.06.024 0040-1625/© 2014 Elsevier Inc. All rights reserved.

- Chronological age is not discriminant when motivations enter the fray. - Usage activities do not necessarily inherit value. - The used scales have validity issues. Drawing on recent research on the service-dominant logic, socioemotional selectivity theory, and data from a two empirical studies, we discuss these three issues separately. 2. Chronological age is not discriminant when motivations enter the fray “This research specifically examines the relationships between age and m-commerce usage activities,” says Chong (2013). The author does indeed find that chronological age impacts the use of m-commerce in China; he states that “the results show that younger users are more likely to use m-commerce for content delivery, transaction-based activities, location-based services and entertainment when compared to older users.”

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One of the most controversial notions about age pertains to the popular belief that there is a normative age-related decline in extrinsic and intrinsic motivation and, consequently, in the individual's behavior (e.g., Kooij et al., 2011; Homburg and Giering, 2001; Lambert-Pandraud et al., 2005; Mägi, 2003). While chronological age has been found to explain all possible changes that occur in people's psychological, social, and even societal functioning in their life cycle, Griffiths (1997) notes that “we should stop accepting chronological age as a factor …” Using the same line of reasoning, several scholars have suggested that chronological age may only serve as a proxy for age-related processes, culminating in Heckhausen et al. (2010), who argue that “chronological age itself does not automatically propel progression through the timetable of development tasks.” As such, chronological aging is only a sub-process of the more general process of aging (Carstensen et al., 1999; Cleveland and McFarlane Shore, 1992; Settersten and Mayer, 1997). Individuals with the same chronological age may differ in important dimensions (e.g., health, family status), at least in the subjective meaning that age has for them (Cleveland and McFarlane Shore, 1992; Settersten and Mayer, 1997). More recent research has thus focused on aging's effect on motivational processes, which can significantly impact information processing (Williams and Drolet, 2005). In this context, socioemotional selectivity theory (SST) has gained much attention in the psychology (Carstensen, 2006; Drolet et al., 2010; Fung and Carstensen, 2003; Hicks et al., 2012) and marketing fields (Jahn et al., 2012; McKay-Nesbitt et al., 2011; Pyone and Isen, 2011; Wei et al., 2012, 2013; Yoon et al., 2005). According to SST, changes in individuals' motivations and behaviors are not primarily due to their physical age, but rather due to changes in their future time perspective (FTP). FTP focuses on individual, subjective time experiences (Husman and Shell, 2008; Lang and Carstensen, 2002) and refers to how much time individuals believe they have left (Cate and John, 2007). Chronological age is therefore negatively related to FTP, but the relationship between the two concepts is usually not linear (Zacher and Frese, 2009). Specifically, SST suggests that the relative importance of a set of social motives changes as a function of their time perspective (Fung et al., 2005). When time is perceived as a limiting factor, emotionally meaningful motives become more important. Conversely, if time is perceived as extended, functional and instrumental goals are likely to be prioritized. Numerous studies support the central SST tenets and FTP's role in particular (e.g., Lang and Carstensen, 2002; Fredrickson and Carstensen, 1990; Fung et al., 1999, 2001; Kuppelwieser and Sarstedt, 2014, in press). Jointly, these studies clearly show that differences between younger and older consumers' behaviors can be more accurately described by their FTP than by their chronological age. These findings have important implications for Chong's study: if the relationship between age and FTP is non-linear for Chinese consumers, we can expect different results when FTP is used as a moderator rather than chronological age. Our first empirical study sheds light on this issue. As the relationship between chronological age and FTP has not yet been examined in a Chinese context, we collected data through an intercept study in Nandan Street, Shanghai. A total of 368 respondents fully completed the questionnaire, which used the Lang and Carstensen (Yoon et al., 2005) scale. Fig. 1 shows a scatterplot of the respondents' chronological age and their FTP. To

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The shaded area represents the 95 percent Bayesian confidence limit

Fig. 1. Penalized spline function for the relationship between age and FTP.

examine the relationship between chronological age and FTP, we fitted a penalized spline function as proposed by Eilers and Marx (1996); this function type ensures high control over smoothness and fit (Wood, 2004). The shadings represent 95% Bayesian confidence bands. As expected, FTP and chronological age are negatively related. However, this relationship is subject to considerable variability—as evidenced by the high dispersion of the data points and the confidence band of the spline function, which decreases wavelike and increases in width for consumers 30 years and older. In a further step, we calculated the T-scores1 and compared them to the reported age as suggested by Lang and Carstensen (2002). While the mean age in this sample was 27.4 years, with the youngest 14 and the oldest 59 years old, 52.4% of the respondents had a T-score below 50 (Lang and Carstensen, 2002). To further assess the distribution of FTP we followed Lang and Carstensen (2002) and conducted a tercile split. This procedure disclosed three groups of participants with limited (lower third, n = 120, T-score mean of 39.27), indefinite (middle third, n = 129, a mean of 49.84), and open-ended (upper third, n = 119, mean of 73.73) time perspectives.2 In light of these results, we would expect differences in the moderating effect between chronological age and the three FTP-groups in Chong's model. Chong recommends that companies should divide their customers into “the young”

1 T-scores characterize and transform the data on a scale between 0 and 100 with a mean of 50 and standard deviation of 10. They are calculated on the basis of z-scores (T = z · 10 + 50) and enhance interpretation and classification of the results (Lang and Carstensen, 2002). 2 We also computed T-scores for the data from a second study conducted in France (see Section 4). The mean age of the second study was 22.3 years and 58.8% of the respondents had a T-score below 50, indicating a limited time horizon. Lang and Carstensen's tercile split resulted in groups of participants with limited (lower third, n = 42, T-score mean of 39.33), indefinite (middle third, n = 49, mean of 49.62), and open-ended (upper third, n = 40, mean of 61.67) time perspectives.

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and “the old,” which is certainly practical, but this approach provides a very restricted perspective—the true sources of motivationally driven behavior (or, more specifically, the usage of m-commerce) lie in consumers' different time perspectives. In this context, it is important to note that FTP is not static and is influenced by life situations, such as illness, frustration, and political situations (Fung et al., 1999; Carstensen and Fredrickson, 1998; Bouffard et al., 1983). Thus, defining several clusters as “the young” and “the old” can be misleading, as different types of FTP can be found in each cluster.

3. Usage activities do not necessarily inherit value Chong's article balances on a fine line. A discussion of the perceived ease of use, the perceived enjoyment, and the perceived usefulness of m-commerce, which are defined as motivation variables, is necessarily linked to the question of what value such activities have for the consumer. As in any other service, m-commerce is a value promise, which influences peoples' behavior. Chong seems to share this notion, as evidenced in his definition of the term “perceived usefulness,” (Chong, 2013) and statements such as “M-commerce adoption studies usually investigate how likely users are to use m-commerce. However, such studies often do not indicate the types of m-commerce activities that users engage in” (Chong, 2013). Nevertheless, a specific usage activity does not always and necessarily inherit value for the customer. Vargo and Lusch (2004) have portrayed value as “perceived and determined by the consumer on the basis of value in use,” rather than by a company merely producing value based on “value in exchange;” that is, by providing goods to customers. In this thinking, users engage and participate in activities which maximize their value-in-use, being defined as value at the time of use, consumption, or experience (Kuppelwieser et al., 2013). Contrary to this service-dominant perspective (e.g., what is the value and how do I receive it?), Chong follows the traditional goods-dominant logic (e.g., how do I use the tool?), which neglects the customer value dimension. More precisely, Chong still focuses on co-production rather than on engaging in a co-creation perspective. While co-creation relates to value that the customer perceives through usage, consumption, or experience, co-production is a component of co-creation (Lusch and Vargo, 2006) and related to tasks and issues that customers undertake prior to or during usage, consumption, or experience. We believe that research on technology adoption and usage urgently needs to consider the latest developments in service thinking (e.g., Kuppelwieser et al., 2013; Zwass, 2010; Vargo and Lusch, 2011; Heinonen et al., 2013; Cova et al., 2011) and take a service-dominant view. In line with recent discussions on services as “processes” (Vargo and Lusch, 2004) as representing a dramatic shift from traditional thinking about the way value is determined and created, Chong's model needs to be developed a step further. More precisely, a critical evaluation of the model— using the co-production and co-creation concepts—will be very fruitful. This approach would differentiate between the model's value-in-use and value-in-exchange dimensions, which take a broader view of customer value as a whole.

4. The used scales have validity issues An important requirement of any scale is that it should sufficiently discriminate between respondents' perceptions regarding the phenomenon under consideration (DeVellis, 2012). If this does not happen, floor or ceiling effects emerge (Kuppelwieser and Sarstedt, 2014). More precisely, a floor (ceiling) effect occurs when the lowest (highest) score on the scale does not capture or discriminate between differences in the lower (upper) end of the measured phenomenon. These effects limit the ability of scales to ferret out high and lowscoring individuals, thus reducing the true range of the scores and biasing any analysis whose computation depends on sample variability. For example, Uttl (Uttl, 2005a) shows that ceiling effects lead to underestimated means, standard deviations, and all variability-dependent indices, such as internal consistency reliability, and correlations with other measures. Chong's measurement of usage activities does not require respondents to rank activities according to their importance. Rather, the author draws on vague quantifiers (“Please indicate if you have engaged with the following m-commerce activities (“1 = never, 2 = rarely, 3 = sometimes, 4 = often, 5 = always”)). As a result, floor and ceiling effects could occur (Uttl, 2005b). As Chong (2013) does not report the means and standard deviation of the used scales, which would have allowed for an assessment of these effects, we collected new data from 131 respondents in France and used the scales which Chong (2013) used.3 Comparable to Chong's approach, we focused on younger respondents. It is important to note that we do not aim to replicate Chong's model; instead, we intend to examine the suitability of the scales for validity by measuring the phenomena under consideration. To test for potential floor and ceiling effects, we follow Uttl (2005a), as well as Wang et al. (2006) and examine the percentages of observations lying at the scale's minimum (lower percentage value) and maximum (upper percentage value). Table 1 shows the result of this analysis. According to Wang et al. (2006), floor (ceiling) effects can be expected if more than 15% of the observations fall into the lower (upper) category. As can be seen, 21 out the 31 items (67.74%) do so. In particular, the items of the content delivery construct exhibit severe ceiling effects as between 33.1% (item content 3: “Read and receive news”) and 57.8% (item content 5: “Use internet search engines”) of the respondents indicated a maximum score on the answering scale. Similarly, the distributions of the first two transaction items (“Perform routine banking services,” and “Transfer money from a preconfigured bank account”) are bimodal in shape, suggesting floor and ceiling effects. While the occurrence of floor and ceiling effects is partly context-specific and depends on factors such as sample composition and respondents' propensity to engage in extreme response behavior (Uttl, 2005b; Sarstedt and Mooi, 2014), our results suggest that caution needs to be exercised when using these scales. Researchers should carry out a pretest and examine item distributions to ascertain whether the scales discriminate well between the respondents. Furthermore, future research should reconsider measuring the motivation

3

See Footnote 2.

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Table 1 Floor and ceiling effects assessment. Scale Item

Mean

Standard deviation

Percentage in lower score (1)

Percentage in upper score (5)

Effect assessment

Ease of use Ease of use 1 Ease of use 2 Ease of use 3 Ease of use 4

4.22 3.89 4.01 3.93

.86 .89 .88 .89

2.3 2.3 1.6 1.6

41.9 24.8 31.8 27.1

Ceiling effect Ceiling effect Ceiling effect Ceiling effect

Perceived usefulness Usefulness 1 Usefulness 2 Usefulness 3 Usefulness 4 Usefulness 5 Usefulness 6

3.41 3.38 3.52 2.70 3.12 3.31

.92 .96 1.00 1.15 1.03 .98

1.6 3.1 3.9 14.2 5.5 3.9

10.9 11.7 16.4 7.9 9.4 10.2

None None Ceiling effect None None None

Perceived enjoyment Enjoyment 1 Enjoyment 2 Enjoyment 3 Enjoyment 4

3.80 3.76 3.70 3.36

.84 .85 .90 1.05

1.6 1.6 1.6 3.9

19.5 16.4 17.2 14.8

Ceiling effect Ceiling effect Ceiling effect Ceiling effect

Content delivery Content 1 Content 2 Content 3 Content 4 Content 5

4.21 4.40 3.98 4.29 3.71

.81 .83 .96 .89 1.18

0 0 1.6 2.3 4.7

39.8 57.8 33.1 49.2 30.5

Ceiling effect Ceiling effect Ceiling effect Ceiling effect Ceiling effect

Transactions Transactions 1 Transactions 2 Transactions 3 Transactions 4

3.09 2.81 2.69 2.13

1.36 1.41 1.56 1.28

16.4 22.7 27.3 42.2

18.0 16.4 14.1 8.6

Floor/ceiling effect Floor/ceiling effect Floor effect Floor effect

Location-based services Location 1 Location 2 Location 3 Location 4 Location 5

3.22 3.25 3.21 3.10 2.96

1.16 1.20 1.19 1.24 1.22

10.9 11.7 11.8 13.4 14.1

11.7 13.3 12.6 11.8 8.6

None None None None None

Entertainment Entertainment 1 Entertainment 2 Entertainment 3

3.34 4.06 4.16

1.23 1.06 .98

8.6 3.1 .8

18.8 42.2 45.3

Ceiling effect Ceiling effect Ceiling effect

For the item wordings, see the Appendix in Chong (2013). n = 131.

variables and usage activity phenomena. Apart from the items' susceptibility to floor and ceiling effects, the measures are related to other thorny issues. First, several of the construct correlations are extremely high, showing values of up to 0.86. Such high inter-construct correlations cast doubt on the measures' discriminant validity, which is not reported in Chong's study. Second, the wording of several items measuring the motivation variables is semantically redundant. While such redundancies translate into alpha values well above 0.90 (Diamantopoulos et al., 2012), they also affect the psychometric properties of the scale (Drolet and Morrison, 2001) adversely and ultimately cast doubt on the scale's content validity (Hair et al., 2014; Rigdon et al., 2011). Third, Chong did not discuss the epistemic nature of the relationship between the construct and the corresponding items, even though a number of researchers have long criticized the lack of explicit measurement specifications underlying most measurement development efforts (e.g., Diamantopoulos et al., 2008; Diamantopoulos and Winklhofer,

2001; Jarvis et al., 2003; MacKenzie et al., 2011). The items measuring the usage activities construct cover distinct—not necessarily related—aspects of content delivery, transactions, and location-based services usage. Chin's (1998) guiding question, “Is it necessarily true that if one of the items (…) were to suddenly change in a particular direction, the others will change in a similar manner?” can be answered with a resounding “no” thus calling for a formative instead of a reflective measurement specification of the constructs. Such measurement misspecification has adverse consequences for the measure's validity. As MacKenzie (2003) warns, “when the measures are formative in nature, dropping items with the lowest item-to-total correlation will result in the removal of precisely those items that would most alter the empirical meaning of the composite latent construct. This can increase the likelihood that a unique part of the conceptual domain will be omitted, make the measures deficient, and undermine construct validity.”

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While it is beyond the scope of this comment to develop a set of valid measures for the constructs under consideration, we want to warn researchers not to blindly adopt these scales. Further research should reconsider these scales, take the potential floor and ceiling effects into account, and explicitly consider the epistemic relationship between the constructs and their measures (Hsu and Fang, 2009). Some of the above-mentioned issues can be resolved by, for example, taking a discrete choice perspective, which would allow for explicitly accounting for trade-offs in the frequency of usage activities. Alternatively, taking an item response theory perspective would allow extreme response behavior to be handled more effectively (de Jong et al., 2008; Raykov and Calantone, 2014). 5. Conclusion Chong's (2013) article takes an important step towards furthering our understanding of the drivers of m-commerce in a changing society, and paves the way to a more service-related model of technology usage. However, as shown in this comment, a broader discussion of the role of FTP and the value relevance of m-commerce activities for customers would provide a richer, potentially more appropriate, picture of the drivers of m-commerce usage and the moderating role of age. Furthermore, researchers should be cautious when drawing on the proposed scales. Owing to potential floor and ceiling effects, as well as issues in the measurement specification, analyses are likely to be substantially biased (Uttl, 2005a; Jarvis et al., 2003). Against this background, it is important to extend and replicate Chong's study, taking the above-mentioned issues into account. Acknowledgment The authors would like to thank Armin Monecke (Ludwig-Maximilians-University, Munich, Germany) for his support with the analyses. References Bouffard, L., Lens, W., Nuttin, J.R., 1983. Extension de la Perspective Temporelle Future en Relation avec la Frustration. Int. J. Psychol. 18, 429–442. Carstensen, L.L., 2006. The influence of a sense of time on human development. Science 312, 1913–1915. Carstensen, L.L., Fredrickson, B.L., 1998. Influence of HIV status and age on cognitive representations of others. Health Psychol. 17, 494–503. Carstensen, L.L., Isaacowitz, D.M., Charles, S.T., 1999. Taking time seriously: a theory of socioemotional selectivity. Am. Psychol. 54, 165–181. Cate, R.A., John, O.P., 2007. Testing models of the structure and development of future time perspective: maintaining a focus on opportunities in middle age. Psychol. Aging 22, 186–201. Chin, W.W., 1998. Issues and opinions on structural equation modeling. Manag. Inf. Syst. Q. 22, xii–xvi. Chong, A.Y.-L., 2013. Mobile commerce usage activities: the roles of demographic and motivation variables. Technol. Forecast. Soc. Chang. 80, 1350–1359. Cleveland, J.N., McFarlane Shore, L., 1992. Self- and supervisory perspectives on age and work attitudes and performance. J. Appl. Psychol. 77, 469–484. Cova, B., Dalli, D., Zwick, D., 2011. Critical perspectives on consumers' role as ‘producers’: broadening the debate on value co-creation in marketing processes. Mark. Theory 11, 231–241. de Jong, M.G., Steenkamp, J.-B.E.M., Fox, J.-P., Baumgartner, H., 2008. Using item response theory to measure extreme response style in marketing research: a global investigation. J. Mark. Res. 45, 104–115.

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Yoon, C., Laurent, G., Fung, H., Gonzalez, R., Gutchess, A., Hedden, T., Lambert-Pandraud, R., Mather, M., Park, D., Peters, E., Skurnik, I., 2005. Cognition, persuasion and decision making in older consumers. Mark. Lett. 16, 429–441. Zacher, H., Frese, M., 2009. Remaining time and opportunities at work: relationships between age, work characteristics, and occupational future time perspective. Psychol. Aging 24, 487–493. Zwass, V., 2010. Co-creation: toward a taxonomy and an integrated research perspective. Int. J. Electron. Commer. 15, 11–48. Dr. Volker G. Kuppelwieser is an Associate Professor in Marketing at the NEOMA Business School (France). His main research interests are group services, organizational service behavior, and individual age perceptions in services. He previously held several positions in the service industry and has 12 year experience of industry. He has published in journals such as Marketing Letters, Human Relations, Journal of Business Research, Managing Service Quality, and Journal of Retailing and Consumer Services, amongst others. He has also given numerous conference presentations and serves as a reviewer for several marketing and organizational behavior journals. Dr. Marko Sarstedt is a Full Professor of Marketing at the Otto-vonGuericke-University Magdeburg (Germany) and Adjunct Professor at the Faculty of Business and Law of the University of Newcastle (Australia). His main research interest is the advancement of research methods to further the understanding of consumer behavior. His research has been published in top-tier journals such as MIS Quarterly, Journal of the Academy of Marketing Science, International Journal of Research in Marketing, Organizational Research Methods, and Journal of World Business. Dr. Sven Tuzovic is an Associate Professor of Marketing at Pacific Lutheran University, School of Business, Tacoma, WA. He was Visiting Professor at Murray State University and at the University of New Orleans. He holds a Doctoral Degree in Marketing from the University of Basel (Switzerland), a Master's Degree from the Catholic University of Eichstaett-Ingolstadt (Germany) and a BBA from Georgia Southern University. His research has been published in several academic journals including the Journal of Services Marketing and in several international conference proceedings. He has won two Best Paper Awards and a Faculty Research Award at Pacific Lutheran University.