The Effects of Task Size and Similarity on the Decision Behavior of Bank Loan Officers

Abstract

Research on decision-making behavior has shown that decision strategies used by individuals are contingent upon the characteristics of the task. For example, as the task size (i.e., the number of alternatives and/or the number of dimensions describing each alternative) increases, individuals tend to quickly eliminate alternatives that do not meet a criterion level for any dimension (i.e., they adopt a noncompensatory decision strategy, in which a high value on one dimension cannot offset or compensate for a low value on another dimension). Most of this research has involved consumers making buying decisions. The purpose of the research reported here was to determine if contingent decision behavior extends to experts (experienced bank loan officers) making business decisions (loan decisions). In this study two task characteristics (task size, and similarity of loan profiles describing alternatives) were varied in a bank loan decision context. Two process tracing methods (information boards and think-aloud verbal protocol analysis) were used to obtain evidence of how eleven bank loan officers made choices among alternative loan candidates. Of particular interest was the effect that changes in task characteristics had on loan officers' decision strategies. The results indicated that when faced with tasks of increasing size, loan officers adapted their behavior in a manner consistent with an increased use of noncompensatory decision strategies. In contrast, when the loan profiles of candidate companies were similar loan officers exhibited an increased use of compensatory strategies. It was also found that when both the task size and similarity of alternatives were varied loan officers adapted their behavior as if they processed these characteristics serially. These results indicate that contingent behavior associated with the two types of task characteristics may be quite different. A priori, there was reason to believe that expert loan officers would not exhibit contingent decision behavior. The fact that their decision strategies were contingent upon task characteristics has important implications for managerial practice and research. First, the results have design implications for information and decision support systems for lending institutions. Second, future research should investigate the consequences of serial processing of task characteristics.expert decision making, contingent behavior, process tracing

Size, entropy, and density: What is the difference that makes the difference between small and large real‐world assortments?

“…Biggs et al (1985) showed that bank loan officers acquired more information and in a more systematic fashion for loan applicants who were very similar to one another on attributes of interest (e.g., similar values on financial statement strength), relative to dissimilar applicants (differences were large). Similarly, Böckenholt et al (1991) observed that decision makers choosing between vacation locations and restaurants searched for more information when the options differed little rather than a lot on attributes of interest (e.g., in temperature, number of rainy days).…”

Decision Making With Information Search Constraints: A Process Tracing Study

“…On the other hand, when a decision task is comparatively simple, decision makers tend to use more complex and effortful decision heuristics such as compensatory decision strategies in which a low value on one attribute can be compensated for by a high value on another attribute. These findings were obtained in studies manipulating time pressure (e.g., Payne et al, 1988;Svenson & Maule, 1993;Wright, 1974;Zakay, 1985), and in studies manipu-lating the numbers of alternatives or attributes (e.g., Biggs et al, 1985;Billing & Marcus, 1983;Kerstholt, 1992;Lohse & Johnson, 1996;Olshavsky, 1979;Payne, 1976;Sundstrom, 1987;Takemura, 1993).…”

Identifying decision strategies: A process- and outcome-based classification method

“…How can we use these metrics to identify decision strategies? We suggest using the metrics in a serial way (for parallel algorithms, see Biggs, Bedard, Gaber, & Linsmeier, 1985;Jarvenpaa, 1989;Todd & Benbasat, 1987). That is, we apply a top-down approach that uses one metric after the other to identify decision strategies.…”