Decision Making on the Small Screen: Adaptive Behavior in Constrained Information Environments

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Decision Making on the Small Screen:
Adaptive Behavior in Constrained Information Environments

Nicholas H. Lurie*

Georgia Institute of Technology

Doe-Hyun Song

Digital Solutions Inc.

Sridhar Narasimhan

Georgia Institute of Technology

* Nicholas H. Lurie is Assistant Professor of Marketing () and Sridhar Narasimhan is Robert A. Anclien Professor (), College of Management, Georgia Institute of Technology. Doe-Hyun Song is Senior Software Engineer at Digital Solutions, Inc.(). The authors thank Zoey Chen and Charlotte Mason for their helpful comments.

More and more, consumers are accessing large amounts of information through small screen devices. For consumers in many countries, the mobile phone is the primary portal to the Internet (Wright 2008). High-speed wireless networks and the growing distribution of ever more powerful cell phones and wireless devices mean that even consumers with access to traditional desktop displays are increasingly using mobile devices to gather product information and make purchases(Alam Khan 2008). Despite a dramatic increase in the usage of mobile devices, little is known about how such devices affect consumer behavior.

Examining how decisions are made in environments that substantially constrain the decision maker’s visual field raises a number of interesting issues not addressed in prior research. For example, most research on decision processes is conducted in environments in which decision makers can view all available information simultaneously (e.g., Bettman et al. 1993; Payne, Bettman, and Johnson 1988) but users of small screen devices can often only see a small portion of the information that desktop users see (Chae and Kim 2004). Prior research on decision making has also focused almost exclusively on how informational characteristics affect the cognitive processes associated with different decision strategies (e.g., Bettman et al. 1993; Jarvenpaa 1989; Lurie 2004; Payne et al. 1988), but small screens also require substantial physical effort (e.g., Albers and Kim 2000; Chae and Kim 2004). Finally, previous research has assumed that the way in which information is displayed to the consumer is under the control of the marketer (Diehl 2005; Häubl and Trifts 2000); yet mobile users view and access information on devices that vary in the amount of information displayed and whether more information is displayed on the vertical versus horizontal axis.These device-specific differences in the visual representation of information likely change the way in which information is used to make decisions (Lurie and Mason 2007; Wedel and Pieters 2007).

This article argues that, when using small screen devices to make decisions, consumers will adapt their decision processes in two important ways: 1) Consumers will account for physical as well as cognitive effort in their information acquisition and decision strategies; and (2) They will adapt their information acquisition and decision strategies to reflect the amount of information that can be accessed simultaneously (i.e., that portion of the underlying information that can be seen without scrolling). If consumers are able to employ decision strategies that minimize effort while maintaining fairly high levels of accuracy (Payne et al. 1988), declines in decision quality when using small screens should be small. If the accuracy of decision strategies depends on whether decision makers process by alternative or by attribute (Bettman et al. 1993), decision quality should be influenced by screen orientation as well as size.

These ideas are tested in two process-tracingexperiments and a Monte-Carlo simulation. Results from the first experiment support the idea that the increased physical effort of making decision on small screen devices leads to reductions in cognitive effort. In particular, a reduction in screen size leads consumers to acquire less information, spend less time per acquisition, spend less time on information acquisition, and be more likely to process by attribute than by alternative. This leads to significant declines in decision quality but these declines are not as large as might be expected given large differences in the amount of information displayed. Experiment results also support the proposal that the constraints of the small screen lead consumers to focus on information that is currently shown, reducing the information reacquisition and decreasing selectivity. Importantly, Experiment 1 also shows that what information is shown is as important as how much information is shown. In particular, screens that show more attributes than alternatives lead to by-alternative processing and higher quality decisions. The second experiment shows that the results found in Study 1 hold regardless of whether attributes are displayed in rows or in columns.

To help explain the experimental results, a Monte-Carlo simulation examined how screen characteristics affect the effort and accuracy of five idealized decision strategies (Johnson and Payne 1985; Payne et al. 1988), that account for physical as well as cognitive processes. Results from the simulation show that screen size has a much smaller effect on decision effort for non-compensatory than compensatory decision strategies. Although effort increases substantially when compensatory decision strategies are employed, increases in effort are minimal for non-compensatory strategies. The Monte-Carlo simulation also explicitly shows how screen orientation changes the effort involved with employing by-attribute or by-alternative strategies and helps explain why changing screen orientation leads to shifts in decision strategies that affect decision quality.

Beyond these substantive implications for decision making on mobile devices in particular, this article contributes to decision-making research more generally. First, the perspective taken here integrates the physical costs of searching for information with the cognitive costs of processing it; suggesting that consumers make tradeoffs between physical and cognitive costs when making decisions. In this way, this article bridges research on information search (Moorthy, Ratchford, and Talukdar 1997; Ratchford, Lee, and Talukdar 2003) and research on how characteristics of information affect decision making (Bettman et al. 1993; Lurie 2004). Second, this article suggests that the visual representation of information; more specifically, depth of field (Lurie and Mason 2007), affects decision strategies. Finally, this article builds on prior research on the use of simulations of human decision making (Bettman et al. 1993; Hastie and Stasser 2000; Johnson and Payne 1985; Payne et al. 1988) to show that simulations can be used to explicitly account for environmental as well as cognitive characteristics when modeling decision processes. This approach can be generalized to assess a wide variety of decision environments in which device characteristics are likely to affect the way in which decisions are made.

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