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A long-standing question is how group perception, which is the perception of a whole group, becomes an exaggerated perception of the individuals who comprise the group. The question receives scant attention within computer-mediated communication (CMC), which is increasingly a communication mode for groups and a research tool to study groups. I address this gap by examining bias in group perception when rating copresence, which is the sense of being together, with the group.

I model bias as occurring when perceivers differentially weigh ratings of individual group members on a variable while rating the whole group on the same variable. I analyzed how the degree of bias in participants’ ratings of copresence with a status-differentiated group varied by the availability of visual cues during CMC in an experiment. I also examined how the group’s status hierarchy impacted bias.

Bias increase as the availability of visual cues decreased and ratings of middle status members were weighed more in group perception than ratings of other members.

Middle status was based on possessing inconsistent statuses. Inconsistency, and not status position, may have rendered these members more salient than others.

Interventions that target group perception may benefit from targeting the group’s middle status members. Researchers and practitioners can minimize bias in group perception through increasing the availability of visual cues in CMC.

The findings illustrate the underpinnings of copresence with an entire group. This is important because copresence shapes several group processes during CMC.

Self-report questionnaires are the predominant method used in human resource management (HRM) research to assess employees’ work-related psychological constructs (e.g., processes, states, and attributes). However, this method is associated with significant shortcomings, including the introduction of self-serving bias and common method variance when used exclusively. In this chapter, the authors challenge the assumption that individuals themselves are the only accurate source of the self-focused information collected in HRM research. Instead, the authors propose that other-ratings – ratings of a target individual that are provided by a workplace observer, such as a coworker, supervisor, or subordinate – can accurately assess commonly measured work-related psychological constructs. The authors begin by explaining the advantages of other-ratings for HRM research and practice, reviewing the history of other-ratings and how they emerged in the personality and person-perception literature, and outlining how they have been used in HRM research to date. Then, the authors build upon Funder’s (1995) realistic accuracy model to develop a theoretical argument detailing why workplace others should be able to accurately judge how another employee thinks and feels about work. Next, the authors highlight existing evidence in the literature on the accuracy of other-ratings and present the results of a preliminary meta-analysis on the ability of other-ratings to predict self-ratings of work-related psychological constructs. Finally, the authors discuss potential moderators of other-rating accuracy and reflect on a number of practical considerations for researchers looking to use other-ratings in their own work. The authors intend for this chapter to meaningfully contribute to the larger conversation on HRM research methods. Other-ratings are a simple, yet powerful, addition to the methodological toolkit of HRM researchers that can increase flexibility in research design and improve the overall quality of research.

In this paper, we aim to provide a comprehensive overview of customer feedback literature, highlighting the burgeoning role of artificial intelligence (AI). Customer feedback has long been a valuable source of customer insights for businesses and market researchers. While previously survey focused, customer feedback in the digital age has evolved to be rich, interactive, multimodal, and virtually real time. Such explosion in feedback content has also been accompanied by a rapid development of AI and machine learning technologies that enable firms to understand and take advantage of these high-velocity data sources. Yet, some of the challenges with traditional surveys remain, such as self-selection concerns of who chooses to participate and what attributes they give feedback on. In addition, these new feedback channels face other unique challenges like review manipulation and herding effects due to their public and democratic nature. Thus, while the AI toolkit has revolutionized the area of customer feedback, extracting meaningful insights requires complementing it with the appropriate social science toolkit. We begin by touching upon conventional customer feedback research and chart its evolution through the years as the nature of available data and analysis tools develop. We conclude by providing recommendations for future questions that remain to be explored in this field.

Purpose – To provide strategic management scholars, particularly graduate students and new faculty members, a novel approach, the lens model, to investigate emerging economies phenomena.

Design/methodology/approach – Based on a review of the strategic management literature and a search of the strategy databases and journals, I propose the lens model approach and discuss its origins, development, and designs since its introduction. It has been used extensively in such fields as cognitive psychology, social psychology, medicine, agriculture, human resources management, and organizational behavior. Besides the wide application, it has relevance for strategic management research.

Findings – An illustrative study and a summary of the approach from a previous study in one prominent journal are also provided as guides. I conclude by providing recommendations on what to consider in using the approach for the study of emerging economies.

Research limitations/implications – In addition to the strengths of the approach, its weaknesses are also discussed. Suggestions on maximizing the potential of the approach are also discussed.

Practical implications – The approach is an invaluable source particularly for graduate students of strategy who often are unfamiliar with microlevel approaches. They can use it to supplement approaches for strategic management.

Originality/value – To my knowledge, this chapter is the first to discuss the lens model approach in the strategic management literature. In that regard, it fills a gap in the research methodology literature. It can therefore help graduate students improve their careers.

For kernel-based estimators, smoothness conditions ensure that the asymptotic rate at which the bias goes to zero is determined by the kernel order. In a finite sample, the leading term in the expansion of the bias may provide a poor approximation. We explore the relation between smoothness and bias and provide estimators for the degree of the smoothness and the bias. We demonstrate the existence of a linear combination of estimators whose trace of the asymptotic mean-squared error is reduced relative to the individual estimator at the optimal bandwidth. We examine the finite-sample performance of a combined estimator that minimizes the trace of the MSE of a linear combination of individual kernel estimators for a multimodal density. The combined estimator provides a robust alternative to individual estimators that protects against uncertainty about the degree of smoothness.