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Inferring decision strategies from clickstreams in decision support systems: a new process-tracing approach using state machines

  • ZfB-Special Issue 4/2012
  • Published:
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Abstract

Webstores can easily gather large amounts of consumer data, including clicks on single elements of the user interface, navigation patterns, user profile data, and search texts. Such clickstream data are both interesting to merchandisers as well as to researchers in the field of decision-making behavior, because they describe consumer decision-behavior on websites. This paper introduces an approach that infers decision-behavior from clickstream data. The approach observes clicks on elements of a decision-support-system and triggers a set of state-machines for each click. Each state-machine represents a particular decision-strategy which a user can follow. The approach returns a set of decision strategies that best explain the observed click-behavior of a user. Results of two experiments show that the algorithm infers strategies accurately. In the first experiment, the approach correctly infers most of the pre-defined decision-strategies. The second study analyzes the behavior of thirty-eight respondents and finds that the inferred mix of decision-strategies fits well the behavior described in the literature to date. Results show that using decision-support-systems on a web site and observing the user’s click-behavior make it possible to infer a specific decision strategy. The proposed method is general enough to be easily applied to both research and real-world settings, along with other decision-support-systems and strategies.

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Authors and Affiliations

  1. Dept. of Information Systems and Business Administration, Johannes Gutenberg-Universität Mainz, Jakob-Welder-Weg 9, 55128, Mainz, Germany

    Jella Pfeiffer,  Malte Probst,  Wolfgang Steitz &  Franz Rothlauf

Authors
  1. Jella Pfeiffer
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  2. Malte Probst
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  3. Wolfgang Steitz
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  4. Franz Rothlauf
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Corresponding author

Correspondence to Jella Pfeiffer.

Appendix

Appendix

DIS (disjunctive rule):

The decision maker uses an alternative-wise approach The alternative that satisfies the aspiration level for at least one relevant attribute is selected If several alternatives fulfill this criterion, one of them is selected at random.

COM (compatibility rule):

The decision maker uses an alternative-wise approach The alternative that satisfies the aspiration levels onk attributes is selected Parameterk is defined by the decision makerk only defines how many attributes have to meet the aspiration level—the decision maker can consider different attributes for each alternative, as long as the number of considered attributes is equal tok for each alternative. (Note that fork = 1 COM is equal to CONJ.).

SAT (satisficing heuristic):

The decision maker examines all alternatives alternative-wise and selects the first alternative that satisfies all aspiration levels If no alternative satisfies all aspiration levels, nothing is selected.

ADD (additive difference rule):

Attributes are compared two at a time The decision maker evaluates all attributes and attribute levels with utility values The overall utility of an alternative is the sum of all the weighted single utility values A weighted single utility value is defined as the product of the utility value of the attribute level and the utility of the corresponding attribute The utility value of an attribute allows the decision maker to rate some attributes as higher than others The alternative with the highest overall utility value is compared with the next alternative These pairwise comparisons are performed until only one alternative is left.

EQW (equal weights rule):

The utility maximizing alternative is selected The decision maker assigns attribute values to all attribute levels The overall utility of an alternative is the sum of the attribute values The strategy is called ‘equal weight’, since only attribute values vary, but attributes are weighted equally.

SAT + (satisficing plus heuristic):

The decision maker uses an alternative-wise approach in arbitrary order The selected alternative is the one whose attribute levels meet the aspirations levels on all of the most important attributes first.

LED (minimum difference lexicographic rule):

The alternative with the best attribute level for the most important attribute is selected Alternatives that are only marginally worse are accepted If several alternatives are equivalent for this attribute, then the second most important attribute is considered etc.

MAJ (majority rule):

The decision maker defines the best attribute level for each attribute The alternative that has the highest number of better attributes is selected.

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Pfeiffer, J., Probst, M., Steitz, W. et al. Inferring decision strategies from clickstreams in decision support systems: a new process-tracing approach using state machines. Z Betriebswirtsch 82 (Suppl 4), 25–46 (2012). https://doi.org/10.1007/s11573-012-0581-0

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