Determinants of instant messaging use for communication with hotels: a policy-capturing study

Purpose Mobile instant messaging (IM) has been increasingly adopted by hotels to communicate with customers. This study aims to explore communication between hotels and customers and identifies the factors that affect hotel customers’ intention to use mobile IM to communicate with hotels. Design/methodology/approach A two-stage exploratory sequential mixed-method design, which combines semi-structured interview and policy-capturing method was applied. Findings The findings indicate that customers are more likely to use mobile IM to communicate with hotels for non-urgent matters; before and after a stay; and if customers are accustomed to using mobile IM for work and non-work purposes in daily life. Research limitations/implications This study goes beyond traditional theories to capturing communication-related factors that affect customers’ IM use in a hotel context. Practical implications The findings indicate why hotel managers should avoid relying on IM as the dominant communication channel. Originality/value This is among one of the first studies that explore customers’ communication needs and communication media choice in hotels.

Combining Process Tracing and Policy Capturing Techniques for Judgment Analysis in an Anti-Submarine Warfare Simulation

The Cognitive Shadow is a prototype decision support tool that can notify users when they deviate from their usual judgment pattern. Expert decision policies are learned automatically online while performing one’s task using a combination of machine learning algorithms. This study investigated whether combining this system with the use of a process tracing technique could improve its ability to model human decision policies. Participants played the role of anti-submarine warfare commanders and rated the likelihood of detecting a submarine in different ocean areas based on their environmental characteristics. In the process tracing condition, participants were asked to reveal only the information deemed necessary, and only that information was sent to the system for model training. In the control condition, all the available information was sent to the system with each decision. Results showed that process tracing data improved the model’s ability to predict human decisions compared to the control condition.

How personal values shape job seeker preference: A policy capturing study

Does the “ideal” organization exist? Or do different workplace attributes attract different people? And if so, what attributes attract what types of employees? This study combines person-organization fit theory and a policy capturing methodology to determine (a) which attributes are the strongest predictors of perceived organization attractiveness in a sample of Australian job seekers, and (b) whether the magnitude of these predictive effects varies as a function of job seekers’ personal values. The design of this study is a randomized experiment of Australian job seekers who responded to an online survey invitation. Each of the 400 respondents received a random subset of 8 of 64 possible descriptions of organizations. Each description presented an organization that scored either high or low on six attributes based on the Employer Attractiveness Scale: economic, development, interest, social, application, and environmental value. Multi-level modelling revealed that all six attributes positively predicted job seekers’ ratings of organization attractiveness, with the three strongest predictors being social, environmental, and application value. Moderation analyses revealed that participants with strong self-transcendent or weak self-enhancement values were most sensitive to the absence of social, environmental, and application value in workplaces, down-rating organizations that scored low on these attributes. Our results demonstrate how job seekers’ personal values shape preferences for different types of workplaces. Organizations may be able to improve recruitment outcomes by matching working conditions to the personal values of workers they hope to employ.

A Test-Retest Reliability Generalization Meta-Analysis of Judgments Via the Policy-Capturing Technique

Policy capturing is a widely used technique, but the temporal stability of policy-capturing judgments has long been a cause for concern. This article emphasizes the importance of reporting reliability, and in particular test-retest reliability, estimates in policy-capturing studies. We found that only 164 of 955 policy-capturing studies (i.e., 17.17%) reported a test-retest reliability estimate. We then conducted a reliability generalization meta-analysis on policy-capturing studies that did report test-retest reliability estimates—and we obtained an average reliability estimate of .78. We additionally examined 16 potential methodological and substantive antecedents to test-retest reliability (equivalent to moderators in validity generalization studies). We found that test-retest reliability was robust to variation in 14 of the 16 factors examined but that reliability was higher in paper-and-pencil studies than in web-based studies and was higher for behavioral intention judgments than for other (e.g., attitudinal and perceptual) judgments. We provide an agenda for future research. Finally, we provide several best-practice recommendations for researchers (and journal reviewers) with regard to (a) reporting test-retest reliability, (b) designing policy-capturing studies for appropriate reportage, and (c) properly interpreting test-retest reliability in policy-capturing studies.

Effects of Situational Factors on Team Planning

Although there has long been consensus in team research that planning generally has a positive impact on performance, very little is known about how input factors (e. g., situational factors) affect the planning behavior of teams. In addition, the various dimensions of planning remain largely unexplored. In this study, we examine the effects of time pressure, task routine, and decision importance on team planning. We suggest that planning consists of four dimensions: exploration, strategic planning, detailed planning, and prognosis. In two policy-capturing studies, undergraduates and employees were presented with a series of hypothetical scenarios and asked to indicate in each case how they might plan for these if working as part of a team. Results from our Bayesian multilevel analyses revealed that teams overall used less planning when they were under acute time pressure, when tasks were very routine, and when the decisions involved were of little importance.

Why Do Banks Favor Employee-Friendly Firms? A Stakeholder-Screening Perspective

We investigate why employee-friendly firms often benefit from lower costs of debt financing. We theorize that banks use employee treatment as a screen to assess firms’ trustworthiness, which encompasses not only confidence in firms’ ability to perform well but also the belief that they will act with good intent toward their creditors. We integrate screening theory and stakeholder theory to explain the—oftentimes unintended—consequences that firms’ actions toward employees have on their relationships with other stakeholders. An analysis of U.S. firms between 2003 and 2010 shows that favorable employee treatment reduces the cost of bank loans, and this relationship is stronger when banks cannot infer firms’ intent from their relations with stakeholders other than employees. A policy-capturing study provides further support that employee treatment serves as a screen for intent. We discuss the implications of our stakeholder-screening perspective as a novel way to understand the second-order, unintended effects of a focal stakeholder relationship on firms’ relations with other stakeholders.

Comparing Two Decision Support Modes Using the Cognitive Shadow Online Policy-Capturing System

The Cognitive Shadow is a prototype tool intended to support decision making by autonomously modeling human operators’ response pattern and providing online notifications to the operators about the decision they are expected to make in new situations. Since the system can be configured either in a reactive “shadowing” or a proactive “recommendation” mode, this study aimed to determine its most effective mode in terms of human and model accuracy, workload, and trust. Subjects participated in an aircraft threat evaluation simulation without decision support or while using either mode of the Cognitive Shadow. Whereas the recommendation mode had no advantage over the control condition, the shadowing mode led to higher human and model accuracy. These benefits were maintained even when the tool was unexpectedly removed. Neither mode influenced workload, and the initial lower trust rating in the shadowing mode faded quickly, making it the best overall configuration for the cognitive assistant.