Determinants of self-report and system-captured measures of mobile Internet use intensity

Abstract

Most research on the first adoption and subsequent use (= acceptance) of Internet access through cellular networks and portable appliances (= mobile Internet) has followed a similar pattern. It has employed survey responses of mobile network operator [MNO] customers to explain consumers' stated future use (continuance) intentions or claimed use intensities related to mobile Internet [MI] access by various beliefs about MI (e.g., perceived relative advantage, usefulness, ease of use). However, there is ample evidence suggesting that MI use intentions and self-reported use intensities are only weakly correlated with actual MI use. Therefore, the present paper develops hypotheses on how the ability of different types of variables to account for variance in MI use intensity may vary depending on whether subjectively estimated or objectively captured use serves as the criterion variable. The hypotheses are tested by analyzing actual MI use behaviors of 300 adopters in Germany, whose mobile IP traffic was extracted from an MNO's billing engine. This "system-captured" criterion measure is integrated with MI adopter responses collected by means of a standardized telephone survey. Results show that the predictors are more strongly correlated with self-rated than with system-captured MI use intensity. Up to 38% of the variance explained in selfrated use may be attributed to artifactual covariance between variables caused by common measurement methods. Factual MI use case features (MI tariff type and appliance class, fixed Internet home access availability) are better able to account for variance in both self-rated and actual MI use intensity than MI related beliefs. The findings imply that variable relationships observed in earlier MI and information system (IS) acceptance studies are likely to have been inflated by common method biases and thus may have provided spurious support for the conceptual frameworks tested. Implications of the results for future MI and IS acceptance research and for MNO seeking to forecast and to influence the MI use intensity of their customers are discussed.

Out of Work, Out of Mind?

“…Participants answered further questions on their smartphone and e-mail use and were asked to estimate the total amount of time spent on e-mail and the number of e-mails read, sent and received outside of work each day. Previous research has reported high correlations between self-reported and actual e-mail behaviors [4], and this is an approach adopted in similar studies. Each question was asked for both the average work day and average day off.…”

SMS use intensity changes in the age of ubiquitous mobile Internet access – A two-level investigation of residential mobile communications customers in Germany

“…First, subjective self-reports of usage intensities of SMS and MI services in general or IP-based mobile instant messaging applications in particular share very limited variance with objective behavioral data derived from traffic measurement or billing systems of MNO or software clients installed on smartphones to capture customer service use patterns (Van den Abeele et al, 2013;Boase and Ling, 2013;Gerpott, 2011;. Thus, self-assessments of use intensities of mobile communication services' inaccurately reflect actual customer behaviors.…”

Strategic signalling and awards: Investigation into the first decade of AIS best publications awards

“…We add the third dimension, method expansion, to show the number of articles associated with different levels of theory building, theory testing, and method expansion (See Table 6). Although majority of the articles (36 out of 45, 80%) do not offer methodological contributions, four studies introduce research method guidelines/principles (code 1) (Agarwal et al 2012;Arazy et al 2010;Bapna et al 2008;Gregor and Jones 2007), one article introduces guiding principles for cross-level theoretical models (code 2) (Burton-Jones and Gallivan 2007), and four studies focus on methodological concerns in conducting research (code 3) (Ba et al 2010;Chi et al 2010;Cyr et al 2009;Gerpott 2011). As shown in Table 6, the most populated category of articles is high theory building/ low theory testing/no method expansion (21 articles), followed by articles associated with high theory building/ high theory testing/no method expansion (10 articles).…”

“…;Gregor and Jones 2007); others offer methodological principles for paying attention to important theoretical concepts (Burton-Jones andGallivan 2007); and some others provide empirical evidence confirming current methodological concerns(Ba et al 2010;Chi et al 2010;Cyr et al 2009;Gerpott 2011). Grouping similar contributions resulted in developing three types of method expansion articles: introducers of research method guidelines (coded as 1), introducers of methodological principles emphasizing existing theoretical constructs (coded as 2), and providers of empirical evidence highlighting methodological concerns (coded as 3).…”