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Decision-making in the equity market has been a challenging task for investors belonging to all age groups. This study aims to find linkages between the decision-making techniques in the equity market and the biological age of the investor.

This is an exploratory study and by surveying a sample of 436 secondary equity investors residing in the Chennai region of India, the study measured the decision-making techniques. Using the ANOVA test, the decision-making technique mostly used by investors belonging to an age category was determined. Further, the linkages were investigated in isolation for the male and female investors. By using regression analysis, a model was developed to predict the actual equity return. The financial characteristics of the various age groups were also analyzed using cross-tabulation.

The results of the study show that the age of the investor plays an important role in the choice of decision-making technique used. The younger investors, who earn the lowest returns were less likely to use industry analysis but more likely to use technical analysis and advocate’s recommendation. Specifically, the younger male investors were less likely to use industry analysis and more likely to use the advocate’s recommendation. The younger female investors were also less likely to use industry analysis. The middle-aged respondents, who earn the highest return were less likely to use technical analysis and advocate’s recommendation owing to lower means. Specifically, the middle-aged male investors were less likely to use the advocate’s recommendation and technical analysis. Then, middle-aged female investors were more likely to use industry analysis.

The findings would be beneficial for financial advisors and wealth managers to suggest age-appropriate strategies for the various investor groups. This age-based profiling of investors would make investors aware of their decision-making techniques and encourage them to make more rational decisions.

The purpose of this study is to identify the critical components of the reverse logistics (RL) disposition decision‐making process and suggest a decision framework that may guide future investigation and practice.

The authors utilized a problem‐driven content analysis methodology. RL literature from 2000 through 2010 was content analyzed to determine which components may impact a firm's RL disposition decision.

The authors extrapolated seven RL disposition decision components from a compilation of 60 variables identified in the literature. Practical implications and suggestions for future research are offered, and a RL disposition decision‐making framework is presented.

Although methodological techniques were carefully followed, the nature of a content analysis may be subject to author bias. Future investigation and use of the framework presented will verify the findings presented here.

This study identifies seven components that should be considered when deciding which RL disposition alternative should be adopted and integrates these components into a decision‐making framework. Supply chain professionals who refer to this framework during the decision process will benefit from a more comprehensive analysis of potential RL disposition alternatives.

Congruent with recent assertions suggesting that RL research is evolving from an operational‐level focus to a holistic business process approach for maximizing value recovery, this study synthesizes operational‐level research to develop a practical framework for RL disposition decision‐making.

The strategic management literature emphasizes the concept of business intelligence (BI) as an essential competitive tool. Yet the sustainability of the firms’ competitive advantage provided by BI capability is not well researched. To fill this gap, this study attempts to develop a model for successful BI deployment and empirically examines the association between BI deployment and sustainable competitive advantage. Taking the telecommunications industry in Malaysia as a case example, the research particularly focuses on the influencing perceptions held by telecommunications decision makers and executives on factors that impact successful BI deployment. The research further investigates the relationship between successful BI deployment and sustainable competitive advantage of the telecommunications organizations. Another important aim of this study is to determine the effect of moderating factors such as organization culture, business strategy, and use of BI tools on BI deployment and the sustainability of firm’s competitive advantage.

This research uses combination of resource-based theory and diffusion of innovation (DOI) theory to examine BI success and its relationship with firm’s sustainability. The research adopts the positivist paradigm and a two-phase sequential mixed method consisting of qualitative and quantitative approaches are employed. A tentative research model is developed first based on extensive literature review. The chapter presents a qualitative field study to fine tune the initial research model. Findings from the qualitative method are also used to develop measures and instruments for the next phase of quantitative method. The study includes a survey study with sample of business analysts and decision makers in telecommunications firms and is analyzed by partial least square-based structural equation modeling.

The findings reveal that some internal resources of the organizations such as BI governance and the perceptions of BI’s characteristics influence the successful deployment of BI. Organizations that practice good BI governance with strong moral and financial support from upper management have an opportunity to realize the dream of having successful BI initiatives in place. The scope of BI governance includes providing sufficient support and commitment in BI funding and implementation, laying out proper BI infrastructure and staffing and establishing a corporate-wide policy and procedures regarding BI. The perceptions about the characteristics of BI such as its relative advantage, complexity, compatibility, and observability are also significant in ensuring BI success. The most important results of this study indicated that with BI successfully deployed, executives would use the knowledge provided for their necessary actions in sustaining the organizations’ competitive advantage in terms of economics, social, and environmental issues.

This study contributes significantly to the existing literature that will assist future BI researchers especially in achieving sustainable competitive advantage. In particular, the model will help practitioners to consider the resources that they are likely to consider when deploying BI. Finally, the applications of this study can be extended through further adaptation in other industries and various geographic contexts.

Grey modeling technique is an important element of grey system theory, and academic articles applied to agricultural science research have been published since 1985, proving the broad applicability and effectiveness of the technique from different aspects and providing a new means to solve agricultural science problems. The analysis of the connotation and trend of the application of grey modeling technique in agricultural science research contributes to the enrichment of grey technique and the development of agricultural science in multiple dimensions.

Based on the relevant literature selected from China National Knowledge Infrastructure, the Web of Science, SpiScholar and other databases in the past 37 years (1985–2021), this paper firstly applied the bibliometric method to quantitatively visualize and systematically analyze the trend of publication, productive author, productive institution, and highly cited literature. Then, the literature is combed by the application of different grey modeling techniques in agricultural science research, and the literature research progress is systematically analyzed.

The results show that grey model technology has broad prospects in the field of agricultural science research. Agricultural universities and research institutes are the main research forces in the application of grey model technology in agricultural science research, and have certain inheritance. The application of grey model technology in agricultural science research has wide applicability and precise practicability.

By analyzing and summarizing the application trend of grey model technology in agricultural science research, the research hotspot, research frontier and valuable research directions of grey model technology in agricultural science research can be more clearly grasped.

A significant body of literature suggests that shipping companies operate in an extremely volatile and risky environment, relying on the effective use of information to remain competitive. However, decision-making in this market is demanding because of the high uncertainty, market competition and significant capital investments. Moreover, the rapid spread of COVID-19 renders information uncertainty a daunting challenge for companies engaged in global trade. Hence, this study aims to explore the information behavior of managers in a time of crisis seems compelling.

This study provides novel insights into the information behavior of senior managers by adopting a qualitative approach. Forty-nine semi-structured face-to-face interviews with individuals from Hellenic shipping companies were conducted. Moreover, this study explores the extant theory qualitatively, using the grounded theory methodology and shows that an unprecedented event (pandemic crisis) can redefine the information behavior of managers.

This study highlights the importance of information in decision-making. Moreover, the results show that, during a pandemic, managers resort to alternative information sources, adopt collaborative information behaviors and take advantage of digital technology.

There is limited research in exploring the information behavior of managers in times of pandemics. This research underscores the fact that during a crisis, managers seek information from digital information resources and decision-making assumes a more decentralized form. This study concludes with a discussion of the theoretical and practical implications of these findings.

This paper describes the forms of knowledge used by players of fantasy sports, games where players create ideal sports teams and compete to accumulate points based on professional athletes’ statistical performances. Messages from a discussion forum associated with a popular fantasy basketball game were analyzed to understand how players described their decision‐making strategies to their peers. The focus of the research was to understand if players use mathematical concepts such as optimization and statistical analyses when assembling their team or if they base their decisions on personal preferences, beliefs, and biases. The analyses in this paper suggest the latter, that players rely on informal, domain‐specific heuristics that often lead to the creation of competitive teams. These heuristics and other forms of player discourse related to knowledge use are described. The paper also suggests ways that analyses of existing practices might provide a foundation for creating gaming environments that assist the acquisition of more formal reasoning skills.

The paper proposes an innovative systemic method helping decision-makers to control servitization transition process, through decision process risk diagnosis.

The proposed method is based on the modeling of decision processes and risk identification and analysis. This method was based on an action-research approach, in close relationship with two companies (SMEs). The paper develops the feasibility experiment at Automelec company.

The method was successfully implemented and delivered concrete diagnosis results.

The generalization of the applicability of the method needs to be tested on several different cases.

The first practical implication is related to the efficiency of the method to help decision-makers in a servitization context to limit uncertainty and get a global view of the weaknesses of their decision-making process, it raises their awareness about servitization transition for their companies. Furthermore, the method also helps to explain the strategy of a servitization transition. It enhances the level of maturity of the decision process of the company, and can be used as a training/learning tool for managers.

The results brought by the research contribute to give the decision-making boards for organization living a servitization transition and especially SMEs a better control over the servitization decision process and related risks, which will increase the economic stability of the company and its vision over long, medium and short horizons. This will bring positive impact on the overall economic and social environment and networks of the servitized SME, and enhance the confidence of coworkers, subcontractors and clients.

The first originality of the paper is related to the new way of considering risk, not only as an analysis criterion but as the central driver in steering a strategic transition for the company, such as servitization. The second originality of the study is about assessing risk occurrence over a decision-making process through decision reliability and decision confidence.

The primary aim of this study is to detail the use of soft computing techniques in business and management research. Its objectives are as follows: to conduct a comprehensive scientometric analysis of publications in the field of soft computing, to explore the evolution of keywords, to identify key research themes and latent topics and to map the intellectual structure of soft computing in the business literature.

This research offers a comprehensive overview of the field by synthesising 43 years (1980–2022) of soft computing research from the Scopus database. It employs descriptive analysis, topic modelling (TM) and scientometric analysis.

This study's co-citation analysis identifies three primary categories of research in the field: the components, the techniques and the benefits of soft computing. Additionally, this study identifies 16 key study themes in the soft computing literature using TM, including decision-making under uncertainty, multi-criteria decision-making (MCDM), the application of deep learning in object detection and fault diagnosis, circular economy and sustainable development and a few others.

This analysis offers a valuable understanding of soft computing for researchers and industry experts and highlights potential areas for future research.

This study uses scientific mapping and performance indicators to analyse a large corpus of 4,512 articles in the field of soft computing. It makes significant contributions to the intellectual and conceptual framework of soft computing research by providing a comprehensive overview of the literature on soft computing literature covering a period of four decades and identifying significant trends and topics to direct future research.

Understanding how leaders make foreign policy and national security decisions is of paramount importance for the policy community and academia. It is our assertion that decisions in these domains can be explained best by tracing the cognitive process leaders go through in formulating and arriving at their decisions, using the Applied Decision Analysis (ADA) method (Mintz, 2005; Mintz & DeRouen, 2010).

Consequently, this chapter summarizes the Applied Decision Analysis method which is utilized throughout the chapters comprising this volume. We then discuss the findings presented in this volume, while demonstrating the merit of both ADA and the poliheuristic theory of decision (Mintz, 2004), in the robust analyses of decisions made by Donald Trump, Vladimir Putin, Recep Tayyip Erdoğan, Benjamin Netanyahu, Winston Churchill, Mao Zedong, Barack Obama, Saddam Hussein, Khaled Mashal, Muammar Gaddafi, Pieter Botha, and Frederik de Klerk. We conclude by providing a brief summary of the case studies which are included in this volume.

The increase of turbulence sources and risk points under the complex social information network has brought severe challenges. This paper discusses risk perception and intelligent decision-making under the complex social information network to maintain social security and financial security.

Cross-modal semantic fusion and social risk perception, temporal knowledge graph and analysis, complex social network intelligent decision-making methods have been studied. A big data computing platform of software and hardware integration for security combat is constructed based on the technical support.

The software and hardware integration platform driven by big data can realize joint identification of significant risks, intelligent analysis and large-scale group decision-making.

The integrated platform can monitor the abnormal operation and potential associated risks of Listed Companies in real-time, reduce information asymmetry and accounting costs and improve the capital market's ability to serve the real economy. It can also provide critical technical support and decision support in necessary public opinion monitoring and control business.

In this paper, the theory of knowledge-enhanced multi-modal multi-granularity dynamic risk analysis and intelligent group decision-making and the idea of an inference think tank (I-aid-S) is proposed. New technologies and methods, such as association analysis, time series evolution and super large-scale group decision-making, have been established. It's also applied in behavior and situation deduction, public opinion and finance and provides real-time, dynamic, fast and high-quality think tank services.