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The purpose of this paper is to empirically test a research model that incorporated antecedents of praise feedback behaviour (fear of confrontation and incentive for reducing nuisance costs), praise feedback behaviour (deliberatively praise feedback, casual praise feedback, and true compliment feedback) and consequences (trust and repurchase intention).

A structural equation model was employed to test the relationships of the research model using survey data collected from 398 Taobao consumers.

The results showed that fear of confrontation and incentive for reducing nuisance costs had a significant positive influence on deliberatively praise feedback and true compliment feedback, respectively, and both antecedents had a significant positive influence on casual praise feedback of consumers. It also showed that trust was influenced negatively by deliberatively praise feedback, and positively by casual praise feedback and true compliment feedback. Meanwhile, deliberatively praise feedback and true compliment feedback were found to have negative and positive influences on repurchase intention, respectively.

This research was a pilot study to identify a three-dimension conceptualization of praise feedback behaviour from the perspective of customer satisfaction, and to understand positive review bias from the perspective of input processes.

In this commentary, the authors follow DeNisi and Sockbeson’s suggestions to reintegrate the organizational feedback and feedback-seeking literatures. They build on and extend their theorizing by suggesting a framework of simultaneous dual judgment processing in both feedback-seeking and organizational feedback interventions..

In the model, evaluation salience plays a role in how performance information is stored (i.e. as online judgments or loose memories), and rater motivation will determine to what extent the rater will deliberately use the stored information to give feedback.

The authors clarify some of the implications of the model for the accuracy of the feedback given, as well as how the cognitive methods that are used can be one of the explaining mechanisms in the link between feedback and performance.

This dual judgment processing approach accounts for the true complexity of the process of organizational feedback that has been largely ignored in past research.

En este comentario seguimos las sugerencias de DeNIsis y Sockbeson de reintegrar las literaturas de feedback organizativo y feedback buscado. Tomando como punto de partida su trabajo lo extendemos para sugerir un marco de procesamiento dual del juicio en las intervenciones tanto de feedback organizativo como de feedback buscado.

En nuestro modelo, la prominencia de la evaluación juega un importante papel sobre cómo se almacena la información sobre el rendimiento (i.e. como juicios online o como recuerdos vagos), y la motivación del evaluador determina hasta qué punto éste usará deliberadamente la información almacenada para proporcionar feedback.

Clarificamos algunas de las implicaciones de nuestro modelo para la precisión del feedback ofrecido, e indicamos como el modo en el que los métodos cognitivos se usan puede ser uno de los mecanismos que explican la relación entre el feedback y el rendimiento.

Esta aproximación dual del juicio toma en consideración la verdadera complejidad del proceso de feedback organizativo que ha sido ignorada en trabajos anteriores.

Procesamiento dual del juicio, Feedback, Evaluación del rendimiento, Búsqueda de feedback, Procesos de feedback, Sesgos

Papel Conceptual

Neste comentário, seguimos as sugestões de DeNIsis e Sockbeson para reintegrar as literaturas de feedback organizacional e feedback procurado. Tomando como ponto de partida este trabalho, nós o estendemos para sugerir uma estrutura de processamento dual do julgamento nas intervenções tanto do feedback organizacional quanto do feedback procurado.

Em nosso modelo, a proeminência da avaliação desempenha um papel importante na forma como as informações sobre o desempenho são armazenadas (ou seja, julgamentos online ou memórias vagas), e a motivação do avaliador determina até que ponto ele usará deliberadamente as informações armazenadas para fornecer feedback.

Esclarecemos algumas das implicações de nosso modelo para a precisão do feedback oferecido e indicamos como a maneira pela qual os métodos cognitivos são usados pode ser um dos mecanismos que explicam a relação entre feedback e desempenho.

Esta abordagem dual do julgamento leva em conta a verdadeira complexidade do processo de feedback organizacional que foi ignorado em trabalhos anteriores.

Processamento de julgamento dual, Feedback, Avaliação de desempenho, Procura de feedback, Processos de feedback, Viés

Papel conceitual

This chapter seeks to help and support online educators in their efforts to improve tomorrow. Specifically, the chapter shares practical strategies and tools that online educators can easily apply, adapt, and/or personalize in order to help promote a mindfully multicultural classroom in their online classrooms and programs. The chapter includes a wide range of actionable tools and exercises to help online instructors optimize the learning experience for all students by building upon the unique strengths and diverse cultural backgrounds of all students in their online classrooms. The strategies help instructors leverage diversity as a means to promote equity and social justice in online programs and, ultimately, the world as a whole. The chapter relies upon Gollnick and Chinn’s (2017) six beliefs that are fundamental to multicultural education and presents strategies from two perspectives or lenses (student-focused and faculty-focused). Approaching the issue from a dual-sided lens is intended to best support the ultimate goal of improving the student learning experience. Emphasis is placed on both public and private interactions between faculty and students. Public interactions include all discussion board and announcement communications. Public interactions also include resources that are shared in the online classroom for all students’ benefit.

Although some research has been carried out on feedback trading in different asset classes, there have been few empirical investigations that consider both major and emerging stock markets (Koutmos, 1997; Antoniou et al., 2005; Kim, 2009) stock index futures (Salm and Schuppli, 2010). In this study, the author examines positive/negative feedback trading in both developed-emerging-frontier-standalone (51) stock markets for 2010–2020 and sub-periods including COVID-19 period.

The hypothesis “feedback trading behaviour led the price boom/bust in the stock markets during the first quarter of COVID-19 pandemic” is tested by employing the Sentana and Wadhwani (1992) framework and using asymmetrical GARCH models (GJRGARCH, EGARCH) in accordance with the empirical literature.

The following conclusions can be drawn from the present study; (1) There is no evidence to support a significant distinction between developed, emerging, frontier or standalone markets or high/upper middle, lower middle income economies in the case of feedback trading. It is more likely to be a general phenomenon reflecting the outcomes of general human psychology (2) in the long term (2010–2020) based on the feedback trading results Asian stock markets appear to be far from efficiency.

Stock markets are selected based on data availability.

Several inferences can be drawn about overall results. First, investors and portfolio managers should beware of their investment decisions during bearish market conditions where volatility is on the rise and also when there is a strong reaction to bad news/negative shocks in the market. Moreover, investing in Asia stock markets may require more attention since those markets are reputed to be more “idiosyncratic”, less reliant on economic and corporate fundamentals in their pricing. Moreover, the impact of foreign investors on stock market volatility and returns and weaker implementation of regulations also affect the efficiency of the markets (Lipinsky and Ong, 2014).

To the best of the author’s knowledge, most studies in the field of feedback trading in stock markets have only focused on a small sample of countries and second, the effect of COVID-19 uncertainty on the stock markets have not been addressed in the literature with respect to feedback trading. This paper fills these literature gaps. This study is expected to provide useful insights for understanding the instabilities in stock markets particularly under conditions of high uncertainty and to fill the gap in the literature by comparing the results for a large sample of countries both in the long term and in the pandemic.

Highlights for review

1. This study has shown that feedback trading is more prevalent in Asian stock markets in the long run in Europe, America or Middle East for the period 2010–2020.

2. Positive feedback traders generally dominated most of the stock markets during the early period of COVID-19 pandemic.

3. Another major finding was that the stock markets in Malaysia, Japan, the Philippines, Estonia, Portugal and Ukraine are dominated by negative feedback traders which may be interpreted as “disposition effect” meaning that they sell the “past winners”.

4. In Indonesia, New Zealand, China, Austria, Greece, UK, Finland, Spain, Iceland, Norway, Switzerland, Poland, Turkey, Chile and Argentina neither positive nor negative feedback trading exists even under uncertain conditions.

This study has shown that feedback trading is more prevalent in Asian stock markets in the long run in Europe, America or Middle East for the period 2010–2020.

Positive feedback traders generally dominated most of the stock markets during the early period of COVID-19 pandemic.

Another major finding was that the stock markets in Malaysia, Japan, the Philippines, Estonia, Portugal and Ukraine are dominated by negative feedback traders which may be interpreted as “disposition effect” meaning that they sell the “past winners”.

In Indonesia, New Zealand, China, Austria, Greece, UK, Finland, Spain, Iceland, Norway, Switzerland, Poland, Turkey, Chile and Argentina neither positive nor negative feedback trading exists even under uncertain conditions.

This paper develops a cross-sectionally augmented distributed lag (CS-DL) approach to the estimation of long-run effects in large dynamic heterogeneous panel data models with cross-sectionally dependent errors. The asymptotic distribution of the CS-DL estimator is derived under coefficient heterogeneity in the case where the time dimension (T ) and the cross-section dimension (N ) are both large. The CS-DL approach is compared with more standard panel data estimators that are based on autoregressive distributed lag (ARDL) specifications. It is shown that unlike the ARDL-type estimator, the CS-DL estimator is robust to misspecification of dynamics and error serial correlation. The theoretical results are illustrated with small sample evidence obtained by means of Monte Carlo simulations, which suggest that the performance of the CS-DL approach is often superior to the alternative panel ARDL estimates, particularly when T is not too large and lies in the range of 30–50.

This study extends prior accounting research on decision aids (DAs) relating to face validity. Specifically, this study aims to examine the effects of face validity through the presence of two levels of bias in DA output. The presence of bias in a DA will not affect how statistically informative an aid is but will decrease the face validity. The findings suggest that non-expert DA users recognize the bias in the DA’s suggestions as evidenced by users’ low agreement with the aid; however, they do not adjust for the bias in their performance, suggesting that non-expert users do not learn from the DA.

This repeated-measures experimental design allows us to examine performance effects over time in response to different levels of bias in the DA output. The participants in the study are provided with outcome feedback to examine learning effects.

The findings suggest that non-expert DA users recognize the bias in the DA’s suggestions as evidenced by users’ low agreement with the aid; however, they do not adjust for the bias in their performance, suggesting that non-expert users do not learn from the DA. Although users of an unbiased DA strongly agree with the DA’s output, individual performance deteriorates over time. Initially, the users of an unbiased DA perform better than those who use a biased DA; however, over time, the performance of users of an unbiased aid deteriorates and the performance of users of the biased aid does not improve.

Companies developing DAs may need to consider the effects of using a DA under circumstances different from those under which the aid was developed and that may lead to the biased DA output. This study has implications for firms that design, develop and use DAs.

This study considers a yet unexamined face validity issue – observable bias in DA output. This study examines deterministic DAs designed to assist the decision-maker through their ability to combine multiple cues in a systematic and consistent manner. This study has implications for firms that design, develop and use DAs. Firms need to consider the effects of using a DA under circumstances different from those under which the aid is developed, thereby, potentially leading to biased DA output. Each additional variable added to the DA will be associated with an incremental cost in a DA’s development, use and modification. The results of this study provide insights contributing to the information available for cost–benefit analyses conducted when developing a DA or when considering the modification of existing aid. Failure to change a DA because of face validity issues alone may result in a decline in user performance. Thus, the cost of modifying a DA must be weighed against the benefits resulting from improved performance. This study contributes insights into how users’ responses to DA bias could affect the assessments of the benefits of including an omitted variable in a DA.

The use of a balanced scorecard (BSC) for performance evaluation is meant to help evaluators make more complete decisions, as they have a variety of financial and non‐financial measures to assess. The problem is that users have difficulty taking all of the measures into consideration. The tendency to place more weight on common measures (measures that are the same across divisions) while either ignoring or placing very little weight on unique measures (measures are unique to a particular division) has become known as a “common measures bias”. The purpose of this paper is to extend a line of research that works to understand how this common measures bias might be mitigated.

This study examines whether the presence of task property feedback, a form of cognitive feedback, prior to a performance evaluation task, can help evaluators overcome the tendency to rely primarily on common measures. This study used a 2×2 experimental design where subjects were asked to evaluate the performance of two managers under either feedback or non‐feedback conditions. In the feedback condition, subjects were provided with their supervisor's suggestions about performance evaluation in the use of BSCs.

This study finds that when subjects were given task property feedback, a form of cognitive feedback, they tended to use more unique measures than if they were not given any feedback information at all.

In practice, more straight forward and simple feedback information is likely easier for companies to implement and easier for evaluators to follow. Feedback information that is too complex or that requires too much effort may frustrate evaluators, at which point they may abandon the effort. The authors' findings also indicate that direct and clear guidance from the top manager of a business may be seen as pressure by lower‐level managers. It is important for top managers to create such a performance evaluation environment so that all BSC measures are considered.

The paper finds that when evaluators judge the performance of managers through the use of a BSC, they tend to weight common measures more heavily than they do unique measures, unless they are provided cognitive feedback to cue them into the importance of using all measures. This study is one of many, following LS (2000), documenting a finding of common measures bias in the use of BSCs. Where this study contributes to the literature is in the use of task property feedback, a form of cognitive feedback, to overcome this bias. Since the use of unique measures is a key attribute of BSCs as they help users capture the nuances of a specific division's or firm's strategy, it is crucial that performance evaluators pay careful attention to them.

Attribution bias can be costly to firms because it hinders decision makers’ ability to infer the real cause of prior events and take corrective action to improve future performance. This study extends prior research by examining whether and how the presence of variance reporting from accounting systems affects firm profitability through a labor cost management decision that is highly susceptible to attribution bias. Our results support the prediction that the presence of variance reporting (process feedback) increases the likelihood of belief revision and corrective action related to the systematic error, and thus increases overall profitability for the firm. The findings of our study propose a solution to attribution and learning problems observed when decision makers are responsible for both cost management and bids as documented in prior literature.

This paper breaks through the limitations of the research on bullwhip effect in the traditional supply chain, extends the research perspective to digital supply chain and discusses the weakening effect of digital supply chain on bullwhip effect by comparing the overall performance of the two.

This paper starts with the weakening mechanism of supply chain digitization on bullwhip effect, builds bullwhip effect models of traditional supply chain and digital supply chain, respectively, simulates the influence of supply chain digitization transformation on bullwhip effect by using Matlab software and analyzes the causes of bullwhip effect in supply chain led by T company and the digitization process.

Firstly, digitization can reduce bullwhip effect in multi-level supply chain by reducing information feedback deviation. Second, digital transformation is conducive to improving the overall performance of the supply chain. Third, government incentives can promote the digital transformation of supply chain and inhibit bullwhip effect.

Although the study considers the heterogeneous subject -- the government's incentive effect on digital transformation and information sharing – it does not include the influence of the end node in the supply chain, that is the consumer. In addition, this paper only analyzes and discusses the bullwhip effect on the amplification of demand, without considering the situation that the market contraction will lead to the reduction of demand.

This paper considers the distortion degree and delay degree of information feedback, carries out quantitative analysis of bullwhip effect, builds the bullwhip effect model of traditional supply chain and digital supply chain, uses Matlab software to analyze the difference of the influence of supply chain digital transformation on bullwhip effect suppression and puts forward the corresponding control strategy.

The research shows that digital transformation can reduce the bullwhip effect in multi-layer supply chain by reducing the information feedback deviation, which is conducive to improving the overall supply chain performance, and government support can accelerate the digital transformation of supply chain to a certain extent.

First, break through the limitations of traditional supply chain research, expand the research perspective to digital supply chain and discuss the weakening effect of digital supply chain on bullwhip effect by comparing the overall performance of the two. Second, quantify the bullwhip effect through information feedback bias and provide an analysis method for the weakening of the bullwhip effect. Third, the driving role of the government in the digital transformation of the supply chain is considered in the study, so that the model is more close to the actual situation of enterprise operation.

Behavioral research is an accepted research paradigm in business disciplines outside of finance including management, marketing and accounting. This paper looks at these disciplines and proposes goals for increasing acceptance of this form of research in real estate. Primary goals include investigation of actual heuristic use, concentration on expert decision makers, either as a group or in comparison to novices, incorporation of additional theory advocating functional heuristics, incorporation of real estate specific theory and identifying both theoretically and empirically when, why and how heuristic use may bias the decision process.