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Using a decision support system (DSS) delays the decision‐making process and commits the user to the cost of invoking the system. The existing configurations of decision support systems do not guarantee the profitability of the DSS. If the DSS generates messages that the decision maker can anticipate, then the cost and waiting time as a result of invoking the DSS will not be justified. Proposes a decision support system equipped with a knowledge‐based model that tells the decision maker, prior to invoking the DSS, whether or not it is profitable to invoke the DSS; if invoking the DSS is not profitable, then the decision maker will have to base the decision on pure managerial subjective judgement. Uses a numerical example to illustrate the work of the proposed DSS.

The objective of this study is to model and analyze the exploration‐exploitation dynamics of March's model of mutual learning in a complex environment. By enhancing the above mentioned model, the paper seeks to propose a new agent‐based model of mutual learning within an organization.

The paper replicates March's model of simulating learning within an organization using an agent‐based simulation approach, and extends it by modelling the problem space as a fitness landscape using Kauffman's NK model technique.

It was found that it is impossible to find a right balance between exploration and exploitation using the communication structure of March's model.

The proposed model could help create a virtual laboratory for experimenting organizations' behavior in a complex co‐evolving environment. This virtual laboratory may be used in the future to support the decision‐making process of managers and policy makers.

Designing the external environment as a fitness landscape helps in discovering what effect the environmental complexity has on the emerging balance between exploration and exploitation. It is the first study to design the environment of a model which analyzes the mutual learning between an organization and its members as a complex non‐linear space.

Consumers’ product evaluations are often influenced by information contained in their memories. Prior to product evaluations, consumers are often exposed to data that permits them to judge the covariation relationships among different product attributes. However, these attribute covariance perceptions may lead to biased product evaluations. Using an experimental design, this study examines the accuracy of consumers’ product attribute covariance beliefs as a function of their product experience and the relevancy of product information to which they are exposed prior to evaluating product performances. The results indicate that even limited product information affects consumers’ beliefs about product performances on attributes for which no information is available. In other words, specific product information may serve as a cue or indicator for other product characteristics via attribute covariance inferences. The accuracy of these inferences appears to be, at least partly, the function of the consumers’ product experience. Consumers with high levels of product experience are more effective at encoding and retrieving product attribute performance information. Implications of the findings are discussed and suggestions for future research are provided.

The purpose of this paper is to develop a novel recommendation method using online reviews with emotional preferences for facilitating online purchase decisions. This leads to better use of information-rich online reviews for providing users with personalized recommendations.

A novel method is developed for producing personalized recommendations in online purchase decision-making. Such a method fuses the belief structure and the Shapley function together to effectively deal with the emotional preferences in online reviews and adequately tackle the interaction existent between product criteria with the use of a modified combination rule for making better online recommendations for making online purchase decisions.

An example is presented for demonstrating the applicability of the method for facilitating online purchase. The results show that the recommendation using the proposed method can effectively improve customer satisfaction with better purchase decisions.

The proposed method can better utilize online reviews for satisfying personalized needs of consumers. The use of such a method can optimize interface design, refine customer needs, reduce recommendation errors and provide personalized recommendations.

The proposed method adequately considers the characteristics of online reviews and the personalized needs of customers for providing customers with appropriate recommendations. It can help businesses better manage online reviews for improving customer satisfaction and create greater value for both businesses and customers.

This study investigated the impact of conspiracy beliefs on fear of Covid-19 and health protective behavior of university students in Pakistan.

A cross-sectional survey using an online questionnaire was conducted at three universities in Punjab (e.g. two public sectors and one private sector) with permission from concerned authorities for data collection. A total of 374 responses were received that were analyzed by applying both descriptive and inferential statistics.

The results indicated the prevalence of conspiracy beliefs and fear of Covid-19 among university students of two public sector universities and one private sector university. Furthermore, the conspiracy beliefs of university students predicted their fear of Covid-19. However, conspiracy beliefs did not predict the health protective behavior of university students.

These results had serious implications for public health in Pakistan demonstrating the critical need for health education and promotion as individual preparedness along with system preparedness is essential to combat Covid-19 pandemic and infodemic. These results are useful for policymakers, healthcare professionals, university administration and library staff for making evidence-based decisions toward health education and promotion related to the Covid-19 pandemic.

It is hoped that the present study would make an invaluable contribution to existing research on promotional health in general and the role of conspiracy beliefs in putting public health at risk in particular as limited studies have been published so far.

Empirical work in macroeconomics almost universally relies on the hypothesis of rational expectations (RE).

This chapter departs from the literature by considering a variety of alternative expectations formation models. We study the econometric properties of a popular New Keynesian monetary DSGE model under different expectational assumptions: the benchmark case of RE, RE extended to allow for “news” about future shocks, near-RE and learning, and observed subjective expectations from surveys.

The results show that the econometric evaluation of the model is extremely sensitive to how expectations are modeled. The posterior distributions for the structural parameters significantly shift when the assumption of RE is modified. Estimates of the structural disturbances under different expectation processes are often dissimilar.

The modeling of expectations has important effects on the ability of the model to fit macroeconomic time series. The model achieves its worse fit under RE. The introduction of news improves fit. The best-fitting specifications, however, are those that assume learning. Expectations also have large effects on forecasting. Survey expectations, news, and learning all work to improve the model's one-step-ahead forecasting accuracy. RE, however, dominate over longer horizons, such as one-year ahead or beyond.

The purpose of this paper is to present an approach which can evaluate the ability that successfully achieves command and control, both in qualitative and quantitative modes, to improve decision accuracy and speed, as well as construct an executable architecture for analyzing and verifying different decision projects.

By defining command and control (C2) decision architecture and decomposing C2 decision processes into measurable subfunctions, measures and metrics will be associated with each of the lowest level decomposed functions, and will be used to provide support for performance evaluation. Both Markov decision process analysis and conditional probability (CP) logic are used for modeling the decision‐making process of course of action (COA). Meanwhile, an executable architecture constructed by Petri net is applied to logic structural verification and performance evaluation.

The paper presents an idea and methodology for net‐centric command and control decision‐making process analysis.

The paper describes and decomposes C2 decision processes for complex missions in uncertain environments.

The paper could be an important reference of analysis and application in net‐centric command and control of decision making.

The paper combines methodology with qualitative methods (decision process decomposition), quantitative method (Markov decision process analysis and CP logic), as well as structural verification and performance evaluation.

This paper analyses the use of big data techniques in auditing, and finds that the practice is not as widespread as it is in other related fields. We first introduce contemporary big data techniques to promote understanding of their potential application. Next, we review existing research on big data in accounting and finance. In addition to auditing, our analysis shows that existing research extends across three other genealogies: financial distress modelling, financial fraud modelling, and stock market prediction and quantitative modelling. Auditing is lagging behind the other research streams in the use of valuable big data techniques. A possible explanation is that auditors are reluctant to use techniques that are far ahead of those adopted by their clients, but we refute this argument. We call for more research and a greater alignment to practice. We also outline future opportunities for auditing in the context of real-time information and in collaborative platforms and peer-to-peer marketplaces.

Whereas much of previous literature focuses upon the impact on yields from the Federal Reserve’s large-scale asset purchases (LSAPs), the purpose of this paper is to study the changes to expected returns.

This empirical investigation offers support for changes to risk premia coincident with LSAPs.

For both equity and bonds, the authors find evidence for supply/demand LSAPs effects; the equity effects are consistent with a substitution effect from bonds to equities, whereas the bond effects appear to be an anomaly.

The findings represent new insight for weighing the efficacy and identifying the scope of LSAPs.

The institutional logics perspective highlights how organizations are embedded within broader systems of meaning and how this embeddedness activates salient institutional logics in organizations that can enable or constrain organizational decisions, practices, and actions. We investigate a core premise of the institutional logics perspective, that of the alignment of institutional logics and organizational practices and design, in the organizational adoption of Corporate Social Responsibility (CSR) practices. We hypothesize that, in the adoption of practices, organizations will house those practices in structural units that align with the logic emphasized by the practice: when adopting practices reflecting a market logic, organizations will locate them in mainline business units, such as marketing; conversely, when adopting practices reflecting a community logic, organizations will locate them in non-mainline business units, such as corporate or philanthropic foundations. Using survey and archival data from 161 Fortune 500 (F500) firms, we find support for our hypotheses. Our findings reveal how institutional logics serve as underlying lynchpins, connecting organizational practices to organizational design so as to reinforce and enable each other.