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The overall goal of science is to build a valid and reliable body of knowledge about the functioning of the world and how applying that knowledge can change it. As personnel and human resources management researchers, we aim to contribute to the respective bodies of knowledge to provide both employers and employees with a workable foundation to help with those problems they are confronted with. However, what research on research has consistently demonstrated is that the scientific endeavor possesses existential issues including a substantial lack of (a) solid theory, (b) replicability, (c) reproducibility, (d) proper and generalizable samples, (e) sufficient quality control (i.e., peer review), (f) robust and trustworthy statistical results, (g) availability of research, and (h) sufficient practical implications. In this chapter, we first sing a song of sorrow regarding the current state of the social sciences in general and personnel and human resources management specifically. Then, we investigate potential grievances that might have led to it (i.e., questionable research practices, misplaced incentives), only to end with a verse of hope by outlining an avenue for betterment (i.e., open science and policy changes at multiple levels).

We take Cumulative Prospect Theory (CPT) seriously by rigorously estimating structural models using the full set of CPT parameters. Much of the literature only estimates a subset of CPT parameters, or more simply assumes CPT parameter values from prior studies. Our data are from laboratory experiments with undergraduate students and MBA students facing substantial real incentives and losses. We also estimate structural models from Expected Utility Theory (EUT), Dual Theory (DT), Rank-Dependent Utility (RDU), and Disappointment Aversion (DA) for comparison. Our major finding is that a majority of individuals in our sample locally asset integrate. That is, they see a loss frame for what it is, a frame, and behave as if they evaluate the net payment rather than the gross loss when one is presented to them. This finding is devastating to the direct application of CPT to these data for those subjects. Support for CPT is greater when losses are covered out of an earned endowment rather than house money, but RDU is still the best single characterization of individual and pooled choices. Defenders of the CPT model claim, correctly, that the CPT model exists “because the data says it should.” In other words, the CPT model was borne from a wide range of stylized facts culled from parts of the cognitive psychology literature. If one is to take the CPT model seriously and rigorously then it needs to do a much better job of explaining the data than we see here.

In Structural Equation Modelling (SEM) cases, a number of steps to ensure that the measurement of the latent constructs, variables, and indicators have been appropriate, reliable, and meet the rules of quantitative research that must be passed by researchers. One of them is the criteria for determining the presence (or not) of a discriminant validity violation, which is still a fierce debate among researchers today. The last recommended method is the Heterotrait–Monotrait ratio of correlation (HTMT) which seeks to revise the previously existing discriminant validity criteria such as the Fornell–Larcker and Cross-loadings criteria. This chapter discusses the implementation of HTMT in the case of manufacturing small and medium enterprises (SMEs) in Indonesia with the aim of providing an illustration of how the HTMT is capable of detecting the violation of discriminant validity and the solutions that can be carried out following the recommendations given by the originators.

Sentiment analysis is a text analysis method that is developed for systematically detecting, identifying, or extracting the emotional intent of words to infer if the text expresses a positive or negative tone. Although this novel method has opened an exciting new avenue for organizational research – mainly due to the abundantly available text data in organizations and the well-developed sentiment analysis techniques, it has also posed a serious challenge to many organizational researchers. This chapter aims to introduce the sentiment analysis method in the text mining area to the organizational research community. In this chapter, the authors first briefly discuss the central role of sentiment in organizational research and then introduce the traditional and modern approaches to sentiment analysis. The authors further delineate research paradigms for text analysis research, advocating the iterative research paradigm (cf., inductive and deductive research paradigms) that is more suitable for text mining research, and also introduce the analytical procedures for sentiment analysis with three stages – discovery, measurement, and inference. More importantly, the authors highlight both the dictionary-based and machine learning (ML) approaches in the measurement stage, with special coverage on deep learning and word embedding techniques as the latest breakthroughs in sentiment and text analyses. Lastly, the authors provide two illustrative examples to demonstrate the applications of sentiment analysis in organizational research. It is the authors’ hope that this chapter – by providing these practical guidelines – will help facilitate more applications of this novel method in organizational research in the future.

A semiparametric stochastic frontier model is proposed for panel data, incorporating several flexible features. First, a constant elasticity of substitution (CES) production frontier is considered without log-transformation to prevent induced non-negligible estimation bias. Second, the model flexibility is improved via semiparameterization, where the technology is an unknown function of a set of environment variables. The technology function accounts for latent heterogeneity across individual units, which can be freely correlated with inputs, environment variables, and/or inefficiency determinants. Furthermore, the technology function incorporates a single-index structure to circumvent the curse of dimensionality. Third, distributional assumptions are eschewed on both stochastic noise and inefficiency for model identification. Instead, only the conditional mean of the inefficiency is assumed, which depends on related determinants with a wide range of choice, via a positive parametric function. As a result, technical efficiency is constructed without relying on an assumed distribution on composite error. The model provides flexible structures on both the production frontier and inefficiency, thereby alleviating the risk of model misspecification in production and efficiency analysis. The estimator involves a series based nonlinear least squares estimation for the unknown parameters and a kernel based local estimation for the technology function. Promising finite-sample performance is demonstrated through simulations, and the model is applied to investigate productive efficiency among OECD countries from 1970–2019.

This study examines the relationship between food experience, emotion, place attachment, and tourists' revisit behavioral intention. A survey questionnaire is conducted on-site in Lukang and distributed to 408 tourists. The dimensions of food experience are established through factor analysis, and a hypothesized model of the relationships between the constructs is tested using structural equation modeling (SEM). The results indicate that tourists' food experiences included local flavor, media recommendation, local learning, life transfer, and interpersonal sharing. In addition, food experience has a significant impact on emotion and place attachment, and emotion has a substantial effect on place attachment. Finally, place attachment significantly impacts tourists' revisit behavioral intention. The study makes a significant theoretical contribution by identifying food experience, emotion, and place attachment as the salient predictors of heritage tourists' revisit intention. Furthermore, the study suggests that food experiences enhance effective bonding at tourism destinations.

This study examined the factors influencing willingness-to-repurchase for each class of airline service, and integrate the constructs of service quality, satisfaction and willingness-to-repurchase which were rooted on Engel-Kollat-Blackwell (EKB) model. The study focuses on the domestic and international arrival of passengers at Murtala Muhammed International Airport in Lagos and Nnamdi Azikwe International Airport in Abuja. Information was gathered from domestic and foreign passengers who had post-purchase experience and had used the airline's services more than once. The survey data were obtained concurrently from arrival passengers at two major international airports using an electronic questionnaire through random and purposive sampling techniques. The data was analysed using the ordinal logit model and structural equation model. From the 606 respondents, 524 responses were received but 489 responses were valid for data analysis and reporting and were obtained mostly from economy and business class passengers. The study found that the quality of seat pitch, allowance of 30 kg luggage permission, availability of online check-in 24 hours before the departing flight, quality of space for legroom between seats, and the quality of seats that can be converted into a fully flatbed are the major service factors influencing willingness-to-repurchase economy and business class tickets. Also, it was found that passengers' willingness to repurchase is influenced majorly by service quality, but not necessarily influenced by satisfaction. These results reflect the passengers' consciousness of COVID-19 because the study was conducted during the heat of COVID-19 pandemic. Recommendations were suggested for airline management based on each class.

Behavioral economics poses a challenge for the welfare evaluation of choices, particularly those that involve risk. It demands that we recognize that the descriptive account of behavior toward those choices might not be the ones we were all taught, and still teach, and that subjective risk perceptions might not accord with expert assessments of probabilities. In addition to these challenges, we are faced with the need to jettison naive notions of revealed preferences, according to which every choice by a subject expresses her objective function, as behavioral evidence forces us to confront pervasive inconsistencies and noise in a typical individual’s choice data. A principled account of errant choice must be built into models used for identification and estimation. These challenges demand close attention to the methodological claims often used to justify policy interventions. They also require, we argue, closer attention by economists to relevant contributions from cognitive science. We propose that a quantitative application of the “intentional stance” of Dennett provides a coherent, attractive and general approach to behavioral welfare economics.