Thursday, March 26, 2009

One More Benefit of Good Web Design

Reference: Parboteeah, D.V., Valacich, J.S., and Wells, J.D. (2008) The influence of website characteristics on a consumer's urge to buy impulsively, Information Systems Research 20 (1), 60-78.

It's nice to know that a website's task-relevant cues (ie., appropriate information and good navigation) and mood-relevant cues (ie., visually appeal) increase consumers' urge to buy impulsively. Not that any designer would ever want to create a website deficient in either of these attributes.

While I have some doubts about the relevance of this study, I have little reason to doubt that improving website quality improves outcomes, including impulsive purchasing. The predicted magnitudes are somewhat suspect due to the nature of the experiments. All experiments were performed with students in a classroom setting and in no case were they actually buying anything -- they were simply reporting on their urge to buy. While students might represent the typical demographic of a web purchaser, the setting is anything but typical.

According to the first experiment, which used structural equation modeling, increasing visual appeal by one standard deviation increases urge to buy by .42 standard deviations and increasing information fit to task by one standard deviation increases urge to buy by .29 standard deviations. According to the second study, which used MANOVA to compare sites that had poor task-relevant and mood-relevant clues to sites that had good clues of task-relevance, mood-relevance, or both, impulsiveness increased from 61.1% of participants, to 72.2% with mood-relevant only, to 74.1% with task-relevant only, to 98% with both. Likewise, the magnitude of intended impulse buying increased from $33.89 to $49.17 to $55.56 to $66.39. Of course, this result is heavily dependent on what's being sold and what the potential impulse buys might be. In the experiment, the intended purchase was a $15 cell phone holster and the possible impulse buys were a $60 bag and several $15 accessories.

Sunday, March 15, 2009

Yet Another Adoption Model

Reference: Dong, L., Neufeld, D.J., and Higgins, C. (2008). Testing Klein and Sorra's innovation implementation model: An empirical examination, Journal of Engineering and Technology Management 25(4), 237-255.

Despite its title, this article is about the adoption of new information systems, not innovation (except to the extent that new systems can be called innovation). I had previously been unaware of Klein and Sorra’s model (Klein, K.J., Sorra, J.S., 1996, Academy of Management Review), which on its face seems similar to, but not as robust as the UTAUT (Venkatesh et al, 2003, MISQ). The main model finds “implementation effectiveness” to be dependent on “user affective commitment” and “implementation climate.” Implementation climate is, in turn, composed of skills, incentives, and the absence of obstacles. User affective commitment is dependent only on “innovation-values fit.”

To motivate their study, the authors cite the oft-reported research that documents how few companies complete their implementations on time, within budget, and with the promised features and functions. Unfortunately, their model does not address many of the common causes of these failures, such as poor estimates of development costs and time, lack of communication between developers and users, inexperience with the technologies employed, etc. Furthermore, their dependent variable, termed implementation effectiveness, is really just a measure of intention to adopt, as it includes five items that address only the following only the following components: 1) Avoidance, “If I can avoid using the system, I do”; and 2) Endorsement, “I think the system is a waste of time and money for our organization (reverse coded)”.

Contributions of the study include scales to measure implementation climate (5 components, 17 items), innovation values fit (3 components, 13 items), skills (6 items), incentives (2 items), absence of obstacles (3 items), and commitment (4 items). Some of these scales are adaptations from other sources. It is worth noting that the variable “innovation values fit” is similar to the construct of “perceived usefulness” in the TAM and TAM2 models, and to elements of “performance expectancy” and “effort expectancy” in the UTAUT model. It’s components are Fit re Quality, such as “The system maintains data I need to carry out my task,” Fit re Locatibility, such as “The system helps me locate corporate or department data very easily,” and Fit re Flexibility and Cooperation, such as “The system supports the repetitive and predictable work processes.”

The study concludes that “when implementation climate is strong and innovation-values fit is present, an implementation was more likely to succeed than when either climate or fit were weak”.

Friday, March 6, 2009

Adaptation to IT-Induced Change

Reference: Bruque, S., Moyano, J., Eisenberg, J. (2008) Individual Adaptation to IT-Induced Change: The Role of Social Networks. Journal of Management Information Systems 25(3), 177-206.

I was interested in this paper because my current research addresses the role of Web-based social networking on innovation and other organizational outcomes. Existing research on Web-based social networking is quite sparse, probably because the technology is so new. So, when I saw the title of this article, I hoped that it would be relevant to my work. It turns out that this research concerns traditional social networks, not Web-based ones, so its relevance to my own research is not direct. Nevertheless, it seems reasonable to extend the authors' conclusions to individuals' extended (Web-based) networks. Thus, it provides some interesting hypotheses for future research.

Even had there been no relevance to my research, I found this article interesting and refreshing. The highlight for me is to see "adaption to IT-induced change" as the dependent variable rather than the common "adoption of technology." There is a significant difference between adoption and adaption, which the authors describe in some depth. Adoption is a binary variable -- either you adopt or do not. Although one can measure the extent of adoption by counting the number of people in an organization who adopt or fail to adopt, adoption remains binary at the individual level. In practice, many changes force employees to adopt to whatever technology is installed, so the real question is how they adapt to these changes. The authors argue and provide references to support the claim that IT-induced changes are harder to adapt to than most other types of change.

The conclusions of the study are not surprising. Adaptation improves the larger the size of the support network and the greater the strength and density of the informational network. The authors defined these networks to include people outside as well as inside the company. This is a significant departure from most studies and makes me optimistic that the results will extend to Web-based social networks. One disconcerting methodological issue is that subjects were allowed to list only five members of their support network and five members of their informational network. I don't believe that there was any measure of the extent to which these networks overlapped. Of course, Web-based social networks are much larger, although they are probably less "strong" or intense.

A significant contribution of the study is the creation of an instrument to measure individual adaptation to IT-induced change.