We posit that Table 14 and its variants lay the groundwork for rigorous testing of any hypothesis regarding the role of heuristics in decision making, especially in the presence of big data. Drawing on the heuristics and biases literature, in this section we propose a few such hypotheses in the context of this dissertation; i.e. organizational decision making on churn. Subsequently we demonstrate how Table 14 and its variants can be used to test the hypotheses. Needless to say, any hypothesis that passes the test in the descriptive analysis could be applied to building a predictive model for organizational decision making (see Chapter Four for example).
To investigate the behavioral economics hypotheses in this section we suggest employing a matched sampling similar to the one suggested in Chapter Four; where the service episodes of non-churners are selected based on the service episodes of the corresponding churners. Specifically, for every churner we randomly select a group of non-churners (that have not been selected by the process yet) whose initial service episodes are longer that the churner’s. Subsequently, we select their service episodes so that (i) the ending of the episodes coincides with the churner’s and (ii) their episodes’ length is equal to the churner’s.
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The first behavioral hypothesis concerns the overall role of peak pain heuristic in a multi-dimensional service quality space; e.g. service peak pain has a positive relationship with churn. It is noteworthy that here, compared to the majority of IS research, we are focusing on the actual churn and not intention to churn. This is again in line with Friedman’s (1953) perspective to the importance of real observed behavior of the firm; “what they do instead of what they say they do.” Consider H1 as a simplified example for this hypothesis. In the next section we elaborate why we note ‘simplified’ for the proposed hypotheses.
H1. Service customers’ odds of attrition is positively associated with the instant service peak pain they experience.
Having Table 14 extracted and selecting the last six weeks of service episode as the action window (see Chapter Four), for each customer we need to first extract 𝑀𝐻1,𝑜𝑣𝑒𝑟𝑎𝑙𝑙 = ∑𝑇𝑡=𝑇−5|𝑚𝑠𝑠(𝑤𝑡)|𝑜𝑣𝑒𝑟𝑎𝑙𝑙; where T is the last week number in the customer’s service episode. This is the number of times that the customer has experienced peak pain with respect all different combinations (both single-dimensional and multi-dimensional) of SQIs during the last six weeks in her service episode. To test the first hypothesis, an ANOVA could be conducted with two treatments (i.e. churners versus non-churners) on 𝑀𝐻1,𝑜𝑣𝑒𝑟𝑎𝑙𝑙 as the dependent variable. The analysis of variance can reveal if on average, churners have significantly experienced more service peak pain in the last six weeks of their service episodes than non-churners.
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Using 𝑀𝐻1,𝑜𝑣𝑒𝑟𝑎𝑙𝑙 to examine the significance of H1 assures us that we have considered all possible combinations on which the peak pain heuristic might be orchestrated.
The second behavioral economics hypothesis that can be investigated with Table 14 concerns the evidence of adaptive toolbox orchestration mechanisms; i.e. if orchestrated heuristics matter in organizational decision making. Consider H2 as a simplified example:
H2. Service customers’ odds of attrition is positively associated with the instant service peak pain orchestrated on two or more service quality dimensions.
To test this hypothesis, two measures should be extracted from Table 14 for each customer; 𝑀𝐻2,𝑠𝑖𝑛𝑔𝑙𝑒 = ∑𝑇𝑡=𝑇−5|𝑚𝑠𝑠(𝑤𝑡)|𝑠𝑖𝑛𝑔𝑙𝑒−𝑑𝑖𝑚𝑒𝑛𝑠𝑖𝑜𝑛𝑎𝑙 and 𝑀𝐻2,𝑚𝑢𝑙𝑡𝑖𝑝𝑙𝑒 = ∑𝑇𝑡=𝑇−5|𝑚𝑠𝑠(𝑤𝑡)|𝑚𝑢𝑙𝑡𝑖−𝑑𝑖𝑚𝑒𝑛𝑠𝑖𝑜𝑛𝑎𝑙. First, an omnibus MANOVA could reveal if the vector of these measure, i.e. [𝑀𝑀𝐻2,𝑠𝑖𝑛𝑔𝑙𝑒
𝐻2,𝑚𝑢𝑙𝑡𝑖𝑝𝑙𝑒], is significantly different for churners; and if yes, follow-up ANOVAs can demonstrate whether churners have significantly experienced more orchestrated service peak pain in the last six weeks of their service episodes than non-churners; or if only single-dimensional service peak pain matters. In case 𝑀𝐻2,𝑚𝑢𝑙𝑡𝑖𝑝𝑙𝑒 is found to be significantly greater for churners than for nonchurners, we can infer that the decision rules in the hypothesized organizational adaptive toolbox can be applied to organizational decision making in an orchestrated fashion.
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Another group of behavioral hypotheses concerns the maximum dimensionality of adaptive toolbox orchestration mechanisms with respect to organizational decisions on churn. That is, what is the maximum number of dimensions on which the service peak pain could be orchestrated in a way that significantly affects organizational decisions on churn? H3 is a simplified example for this series of hypotheses:
H3. Service customers’ odds of attrition is positively associated with the instant service peak pain orchestrated on three or more service quality dimensions.
This could be viewed as a sensitivity analysis on the number of orchestration dimensions. Again, a variant of Table 14 can facilitate this analysis. Specifically, we need to drill down on the cardinality of Table 14; e.g. having tuples like <…, Single Dimensional Cardinality, Two Dimensional Cardinality, Three and More Dimensional Cardinality>. We suspect this is an important research question in the context of heuristics and biases. That is, the hypothesis addresses the limitations on the orchestration mechanisms of the adaptive toolbox decision rules, which are hypothetically invoked when at least one of the information processing limitations is in place.
5.6. Compressed Skycube and Adaptive Toolbox Orchestration; Still a Missing Piece