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Accurate forecasting has become a challenge for companies operating in today's business environment, characterized by high uncertainty and short response times. Rapid technological innovations and e‐commerce have created an environment where historical data are often of limited value in predicting the future. In business organizations, the marketing function typically generates sales forecasts based on judgmental methods that rely heavily on subjective assessments and “soft” information, while operations rely more on quantitative data. Forecast generation rarely involves the pooling of information from these two functions. Increasingly, successful forecasting warrants the use of composite methodologies that incorporate a range of information from traditional quantitative computations usually used by operations, to marketing's judgmental assessments of markets. The purpose of this paper is to develop a framework for the integration of marketing's judgmental forecasts with traditional quantitative forecasting methods. Four integration methodologies are presented and evaluated relative to their appropriateness in combining forecasts within an organizational context. Our assessment considers human factors such as ownership, and the location of final forecast generation within the organization. Although each methodology has its strengths and weaknesses, not every methodology is appropriate for every organizational context.

The present paper’s aim lies in providing an empirical analysis of whether the loan officers’ psychological traits display an explanation of their subjective prediction accuracy.

A qualitative and qualitative analysis has also been applied.

The reached results reveal that, with respect to microfinance institutions, the loan officers’ accurate subjective judgment crucially relies on the principle of learning-through-experience so as to construct a special type of relevant skills and competences. Learning is both an intellectual and an emotional process, whereby loan officers acquire certain specific experience likely to enhance their cognitive skills and shape their emotional intelligence, which would, in turn, sharpen their forecasting accuracy. In fact, the higher emotional intelligence is, the easier it makes it for loan officer to adjust or reduce their judgmental errors and make a more effective application of the pertinent heuristics. Conversely, however, the lack or absence of emotions and feelings of novice loan officer is likely to hinder and inhibit the cognitive as well as the learning processes.

The paper considers the role of individual psychological traits on the decisions of experienced and inexperienced individuals when deciding on the default risk in the context of loan decisions. Learning is both an intellectual and an emotional process, whereby loan officers acquire certain specific experience likely to enhance their cognitive skills and shape their emotional intelligence, which would, in turn, sharpen their forecasting accuracy.

The purpose of this paper is to investigate how individual interviewers’ dispositional cognitive motivations may influence interview interactions and outcomes. More specifically, this study explores the influence of the need for cognition, need for cognitive closure, and accountability on the relationship between first impressions and selection decisions.

In total, 41 graduate students were assigned the role of interviewers and were tasked to interview 331 undergraduate students at a large Midwestern university. The selection interview was designed to recruit qualified undergraduate students to the MBA program of the university.

First impressions significantly influenced selection decisions, but did not influence interviewers’ behaviors. Moreover, multilevel analyses reveal that interviewers’ need for cognition and accountability moderate the relationship between first impression and selection decisions, albeit in different direction. Need for cognition strengthens, whereas accountability weakens the relationship between first impression and selection decision.

A potential interviewer bias is apparent, where interviewers high on need for cognition tend to weight first impressions more in the decision process. However, this bias was not directly observable, since interviewers’ behaviors during the interview were not affected by first impressions.

The present study goes beyond previous research on first impressions in the employment interview, finding that dispositional differences account for the tendency to weigh first impressions in the selection decision.

This research aims to explore and explain the determinants of irrational financial decision making, especially the day‐of‐the week anomaly, by using psychological approach.

As it is a conceptual paper, this research explores the psychological biases literature and links it to the day‐of‐the week anomaly. Using Ellis' ABC (Activating Event, Belief, and Consequences) Model, the authors survey and classify the stimulant as the occasion that stimulates the psychological biases of investors, and these psychological biases will bring a consequence in behaviour which is irrationality in weekend effect.

Adopting Ellis' ABC model, the paper constructs a theoretical framework that link the psychological biases and day‐of‐the week anomaly. The theoretical model is also given as a proposed model for future empirical research.

This paper contributes to research by giving the theoretical model and its framework. The latter, future research can examine the proposed psychological biases as the determinant of day‐of‐the week anomaly empirically.

This paper conceptually builds a framework and derives a proposed equation model linking the psychological biases (weather, moon, attention bias, heuristic bias, regret, and cognitive bias) to the day‐of‐the week anomaly.

The purpose of this paper is to try to understand better whether performance appraisal (PA) helps performance evaluators (PEs) to manage more effectively and meet employees' expectations in US‐based corporations.

A 54‐item research instrument was developed and implemented using structured interviews with 54 PEs, who worked at five US‐based corporations (Aetna Insurance, IBM, Johnson & Johnson, Valspar, Wyeth Pharmaceuticals). Responses were statistically analyzed with descriptive statistics and decision trees.

Time dedicated to implementing PA was the most important factor leading to ethical issues. PEs with the highest educational levels and most experience spent the least amount of time (1.86 vs 3.19 hours) implementing PA. Most PEs (79.6 percent) solicited feedback about employees' performance from employees' peers but 20 percent did not. Additionally, not a single PE had PA as a specific objective, making it difficult to sequester time necessary for PA. Older PEs felt PA helped them manage more effectively and PEs who were Black or White and from Marketing/Sales were most favorable about meeting employees' PA expectations. There were no remarkable differences among PA systems at the five corporations, e.g. 360‐degree training.

Structured interviews required delicate interaction due to sensitivity about the US economy and resulting layoffs within interviewees' corporations.

PEs, particularly older managers with higher educational levels, should have a PA objective and be held accountable to it to ensure that they dedicate time necessary to complete PA in the way the PA system intends.

The paper provides insight about PA within the US corporate setting and will be highly interesting to those in that field.

Course evaluations are formative and are used to evaluate learnings of the students for a course. Anomalies in the evaluation process can lead to a faulty educational outcome. Learning analytics and educational data mining provide a set of techniques that can be conveniently applied to extensive data collected as part of the evaluation process to ensure remedial actions. This study aims to conduct an experimental research to detect anomalies in the evaluation methods.

Experimental research is conducted with scientific approach and design. The researchers categorized anomaly into three categories, namely, an anomaly in criteria assessment, subject anomaly and anomaly in subject marks allocation. The different anomaly detection algorithms are used to educate data through the software R, and the results are summarized in the tables.

The data points occurring in all algorithms are finally detected as an anomaly. The anomaly identifies the data points that deviate from the data set’s normal behavior. The subject which is consistently identified as anomalous by the different techniques is marked as an anomaly in evaluation. After identification, one can drill down to more details into the title of anomalies in the evaluation criteria.

This paper proposes an analytical model for the course evaluation process and demonstrates the use of actionable analytics to detect anomalies in the evaluation process.

The purpose of this paper is to investigate the effects of personality dimensions (conscientiousness, neuroticism, extraversion, agreeableness, openness to experience, locus of control) on the efficacy of judgmental adjustments of statistical forecasts.

This paper uses a two-level hierarchical linear model to analyze a large data set obtained from an organizational setting (a quick service restaurant chain) that includes 3,812 judgmental adjustments of sales forecasts made by 112 store managers.

The results indicate that the average forecast accuracy improves as a result of judgmental adjustments, but performance of individual forecasters varies considerably based on their personality. Specifically, the trait of openness to experience tends to improve forecast accuracy while extraversion and external locus of control have negative effects.

Integration of human judgment with analytics algorithms is a major challenge for organizations. Documenting the impact of these traits on forecast accuracy opens the door for forecasting support system design, training, personnel selection and correction strategies that can be applied to judgmental adjustments.

The purpose of this study is to illustrate the potentially detrimental effects on audit decision-making of certain judgmental heuristics, which can lead to systematic judgmental biases. This paper provides background on the heuristics and biases approaches to decision-making to increase auditors' awareness of the anchoring and adjustment effects affecting audit judgments adversely.

This study reports the results of an experimental research design analyzing the audit judgment of 85 auditors in Switzerland.

Based on the results of the experiment, the results indicate evidence on the existence of the anchoring and adjustment heuristic in Swiss audit judgments. The authors could identify an influence of the audit company size, the auditors' experience and the auditors' knowledge about behaviorism and anchor heuristic with regard to the anchoring and adjustment effect on audit judgment.

The experimental tasks were relatively simple abstractions from the more complex analytical review situations faced by practicing auditors. Due to the small sample size, the authors cannot ensure representativeness of the results.

Professional judgment is a skill that auditor acquires overtime, combined with experience and knowledge, that allows him to achieve reasonable judgments, being independent of other opinions and free from material biases in a given circumstance. Our results show that auditors who are aware of biases and heuristics are less prone to judgment biases.

This paper is the first to analyze the impact of auditors' explicit experience and knowledge about behaviorism and anchor heuristic on the anchoring and adjustment effect on audit judgment. Through a stronger awareness of cognitive biases, a professional skepticism can be enhanced.

The purpose of this study is to examine how trends in historical data influence two types of predictive judgments: territory selection and salesperson hiring. Sales managers are confronted frequently with decisions that explicitly or implicitly involve forecasting with limited information. In doing so, they conceptualize how the magnitude of these trend effects may be affected by the experience managers have in making these types of judgments. Study 1 provides evidence of a curvilinear relationship between experience and reliance on the trend data whereby the sales territory selections of novice sales managers exhibited greater susceptibility to informational trends than did the evaluations of naïve and expert decision-makers. A benchmark analysis in Study 2 further revealed that the salesperson selections made by novice and expert sales managers were equally biased, albeit in opposite directions, with novices overweighting and experts underweighting historical performance trends. Implications of these findings are discussed, as are avenues for future research.

The authors employ an online experimental design methodology of practicing managers. For Study 1, they use regression, whereas Study 2 uses a deterministic process to develop a priori predictive benchmark forecasts. Ordinary least squares is then used to estimate manager’s decisions, which are then compared to the predictive forecasts to determine accuracy.

Study 1 provides evidence of a curvilinear relationship between experience and reliance on the trend data whereby the sales territory selections of novice sales managers exhibited greater susceptibility to informational trends than did the evaluations of naïve and expert decision-makers. A benchmark analysis in Study 2 further revealed that the salesperson selections made by novice and expert sales managers were equally biased, albeit in opposite directions, with novices overweighting and experts underweighting historical performance trends.

The present inquiry is the first to provide insights into an important issue that has been the subject of equivocal findings, namely, whether experience in a judgmental domain exerts a facilitating or debilitating effect on sales manager decision-making. In this regard, some research supports the intuition that experience in making a particular type of decision can insulate managers from judgmental bias and, in doing so, improve decision quality (see Shanteau, [1992a] for a summary). In contrast, other work provides a more pessimistic view by demonstrating that the quality of decision-making is either unaffected by or can erode with additional experience (Hutchinson et al., 2010). To help reconcile these conflicting findings, the authors presented and tested a theoretical framework conceptualizing how trends may influence predictive judgments across three levels of decision-maker experience.

This paper synthesizes the extant research on the outcome effect in the accounting domain, focusing primarily on the context of performance evaluation. It reviews the current state of our knowledge about this phenomenon, including its underlying cognitive and motivational causes, the contexts in which the outcome effect is observed, the factors that influence its various manifestations, and ways in which undesirable outcome effects can be mitigated. It also considers various perspectives about the extent to which outcome effects represent undesirable judgmental bias, and whether this distinction is necessary to motivate research on this topic. The paper is intended to motivate and facilitate future research into the effects of outcome knowledge on judgment in the accounting context. Therefore, we also identify important unanswered questions and discuss opportunities for future research throughout the paper. These include additional consideration of instances in which the outcome effect is reflective of bias, how this bias can be effectively mitigated, ways in which outcome information influences judgment (regardless of whether this influence is considered normative), and how the underlying causes of the outcome effect operate singly and jointly to bring about the outcome effect. We also consider ways that future research can contribute to practice by determining how to encourage evaluators to retain and incorporate the relevant information conveyed by outcomes, while avoiding the inappropriate use of outcome information, and by enhancing external validity to increase the generalizability of experimental results to scenarios frequently encountered in practice.