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

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 self-rated 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.