Management Decision最新文献

Purpose

This paper develops an evidence-based conceptual framework for exploring the applicability of choice architecture in business relationships.

Design/methodology/approach

Empirical studies of choice architecture in various fields are systematically reviewed to identify choice architecture techniques; major resulting categories of techniques are then discussed for applicability to business relationship contexts, and avenues for research are outlined.

Findings

Managerial decision-making is potentially influenced by changes to choice contexts that (1 provide additional information from outside the choice context, (2) facilitate a particular way of comparing options, (3) present options in new ways, (4) rework the choice set, e.g. by adding options and (5) create internal states that push toward a target choice.

Originality/value

The paper introduces the concept of choice architecture to business relationships. It develops a taxonomy of choice architecture techniques to guide the exploration of choice architecture effects in business relationships.

Purpose

Organizational sustainability in the changing market requires adequate management of resources and opportunities; thus, entrepreneurship and digitization have acquired special relevance as key elements in the training programs offered for employee’s development. In order to analyze whether employee motivations influence corporate entrepreneurial development and consider the influence of this type of training program on intrapreneurial activity, a survey of 241 small and medium-sized enterprises (SMEs) in the Community of Madrid was carried out.

Design/methodology/approach

The analysis was conducted using the fuzzy set qualitative comparative analysis (fsQCA) methodology.

Findings

This analysis provides a new perspective for the research of the training programs proposed and puts forward recommendations to achieve an approach that favors entrepreneurial development. The paper concludes by discussing the implications for managers and business leaders and showing actions to integrate digitalization with transversal value for continuous and sustainable development.

Originality/value

The value of this article lies in demonstrating that training in entrepreneurial skills or digital tools is not a necessary variable for the presence of entrepreneurship in the organization; however, an absence of training in these areas does imply that intrapreneurial characteristics do not develop.

Purpose

This study demonstrates how artificial intelligence (AI) shapes the strategic planning process in volatile, uncertain, complex and ambiguous (VUCA) business environments. Having adopted various domains of the Cynefin framework, the research explores AI's transformative potential and provide insights regarding how organisations can harness AI-driven solutions for strategic planning.

Design/methodology/approach

This conceptual paper theorises the role of AI in strategic planning process in a VUCA world by integrating extant knowledge across multiple literature streams. The “model paper” approach was adopted to provide a theoretical framework predicting relationships among considered concepts.

Findings

The paper highlights potential application of the Cynefin framework to manage complexities in strategic decision-making process, the transformative impact of AI at different stages of strategic planning, the required strategic planning characteristics within VUCA to be supported by AI and the attendant challenges posed by AI integration in the uncertain business landscape.

Originality/value

This study pioneers a theoretical exploration of AI's role in strategic planning within the VUCA business landscape, guided by the Cynefin framework. Thus, it enriches scholarly discourse and expands knowledge frontiers.

Purpose

Scholars have underscored the importance of organizational authenticity, but it is unclear how it influences the links among market strategy, and nonmarket strategy (NMS) and firm performance. This study addresses this gap in the literature.

Design/methodology/approach

A survey of 294 managers in firms based in the United States investigates configurations among competitive strategy (e.g. cost leadership or differentiation), political and social nonmarket strategy (NMS), authenticity, and firm performance.

Findings

Cost leaders tend to engage in political nonmarket strategy (PNMS), but the interaction does not necessarily improve firm performance. Differentiators are more likely to pursue social nonmarket strategy (SNMS) and perform better, but neither market-nonmarket strategy configuration is inherently optimal.

Research limitations/implications

The results support market-nonmarket strategy configurations but do not prescribe optimal combinations. However, the sample is cross-sector and employs self-reports for firm performance.

Practical implications

Political and social authenticity can enhance firm performance, but nonmarket activity can compromise a firm’s ability to be politically and socially authentic. Authenticity can drive performance, but a firm’s nonmarket activity can compromise its ability to be politically and socially authentic. Firms should view a prospective loss in authenticity as a potential cost of nonmarket activity.

Originality/value

This paper investigates how a firm’s emphasis on market (competitive) strategies, political and social nonmarket strategies, and political and social authenticity impact financial and non-financial performance. It also tests the veracity of two market-nonmarket configurations, cost leadership with political NMS and differentiation with social NMS.

Purpose

This study investigates the relationships between environmental, social, and governance (ESG) controversies and insolvency risk in the insurance sector. Drawing from legitimacy and stakeholder theories, the authors explore the impact of ESG controversies on insurers’ insolvency risk and the moderating effect of ESG practices on this relationship.

Design/methodology/approach

This study utilises a dataset comprising 120 stock insurance firms spanning from 2011 to 2022. The authors employed system-GMM estimations to control for potential endogeneity and conducted several robustness checks.

Findings

ESG controversy positively influences insurers’ insolvency risk, with ESG practices mitigating these positive effects. The Governance (G) component of ESG practices plays a key role in counteracting the effects of ESG controversies on insurance companies’ insolvency risk.

Originality/value

This is the first study to investigate the direct relationship between ESG controversies and insolvency risk in the insurance industry. It underscores the critical influence of stakeholders’ perceptions of the company’s legitimacy, which is determined by the number of ESG controversies undertaken by the insurer company, on its insolvency risk. Additionally, by examining the three components of ESG practices individually, the authors offer insights into how managers can gain a competitive edge, particularly by utilising governance practices as safeguards against the adverse effects of ESG controversies on their financial risk.

Purpose

Although many countries are focusing on the management of construction and demolition waste (CDW) resource utilization, the effect of risk aversion of the green innovation-led enterprise on the performance of the CDW resource utilization supply chain is unclear when considering different green innovation contexts (green innovation led by the building materials remanufacturer or by the construction waste recycler). This study aims to investigate how the level of risk aversion of the green innovation-led enterprise affects CDW resource utilization under different green innovation contexts based on contingency theory.

Design/methodology/approach

Using Stackelberg game theory, this study establishes a decision model consisting of a building materials remanufacturer, construction waste recycler and CDW production unit and investigates how the level of risk aversion of the green innovation-led enterprise under different green innovation contexts influences the performance level of the supply chain.

Findings

The conclusions are as follows. (1) For the green innovation-led enterprise, the risk-averse behaviour is always detrimental to his own profits. (2) For the follower, the profits of the construction waste recycler are negatively correlated with the level of risk aversion of the green innovation-led enterprise in the case of a small green innovation investment coefficient. If the green innovation investment coefficient is high, the opposite result is obtained. (3) When the green innovation investment coefficient is low, the total supply chain profits decrease as the level of risk aversion of the green innovation-led enterprise increases. When the green innovation investment coefficient is high, total supply chain profit shows an inverted U-shaped trend with respect to the degree of risk aversion of the green innovation-led enterprise.

Originality/value

(1) This study is the first to construct a green innovation context led by different enterprises in the CDW resource utilization supply chain, which provides a new perspective on green management and operation. (2) This study is the first to explore the operation mechanism of the CDW resource utilization supply chain based on contingency theory, which provides new evidence from the CDW resource utilization supply chain to prove contingency theory. At the same time, this study examines the interactive effects of the green innovation cost coefficient and the degree of risk aversion of green innovation-led enterprises on the performance of supply chain members, expanding the contingency theory research on contingencies affecting enterprise performance. (3) This study will guide members of the CDW resource utilization supply chain to rationally face risks and achieve optimal supply chain performance.

Purpose

Artificial intelligence (AI) can help in defining preventive strategies in taking decisions in complex situations. This paper aims to research how workers might deal with intervening AI tools, with the goal of improving their daily working decisions and movements. We contribute to deepening how workers might deal with intervening AI tools aiming at improving their daily working decisions and movements. We investigate these aspects within a field, which is growing in importance due to environmental sustainability issues, i.e. waste management (WM).

Design/methodology/approach

This manuscript intends to (1) investigate if AI allows better performance in WM by reducing social security costs and by guaranteeing a better continuity of service and (2) examine which structural change is required to operationalize this predictive risk model in the real working context. To achieve these goals, this study developed a qualitative inquiry based on face-to-face interviews with highly qualified experts.

Findings

There is a positive impact of AI schemes in helping to detect critical operating issues. Specifically, AI potentially represents a tool for an alignment of operational behaviours to business strategic goals. Properly elaborated information, obtained through wearable digital infrastructures, allows to take decisions to streamline the work organization, reducing potential loss due to waste of time and/or physical resources.

Research limitations/implications

Being a qualitative study, and the limited extension of data, it is not possible to guarantee its replication and generalizability. Nevertheless, the prestige of the interviewees makes this research an interesting pilot, on such an emerging theme as AI, thus eliciting stimulating insights from a deepening of information coming from respondents’ knowledge, skills and experience for implementing valuable AI schemes able to an align operational behaviours to business strategic goals.

Practical implications

The most critical issue is represented by the “quality” of the feedback provided to employees within the business environment, specifically when there is a transfer of knowledge within the organization.

Originality/value

The study focuses on a less investigated context, the role of AI in internal decision-making, particularly, for what regards the interaction between managers and workers as well as the one among workers. Algorithmically managed workers can be seen as the players of summarized results of complex algorithmic analyses offered through simpleminded interfaces, which they can easily use to take good decisions.

Purpose

This article aims to assess risks related to the supply chain 5.0 digitalization. It aims to analyze interdependencies and causal relationships between critical digital supply chain 5.0 risks, emphasizing the need for proactive management to address emerging challenges.

Design/methodology/approach

Through an extensive literature review and expert judgment, risks related to supply chain 5.0 digitalization are identified. An integrated approach for risk assessment is employed, where the Analytic Hierarchy Process (AHP) is utilized to prioritize these risks. Subsequently, the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method is employed to investigate cause-and-effect relationships among the identified top 10 risks. This comprehensive analysis forms the basis for informed strategic management decision-making.

Findings

The analysis identifies significant influences of “Dependence on technology,” “Complexity”, “Potential system failures”, and “Cyber security” while “Environmental impact” and “Socio-economic disparities” emerge as prominent risks in supply chain 5.0 digitalization. These findings offer actionable insights for management decision-making, guiding the formulation of strategies to address and mitigate critical risks.

Practical implications

The proposed integrated approach (AHP-DEMATEL) provides valuable insights for managers to effectively mitigate digital supply chain 5.0 risks and strategically respond to disruptions. By prioritizing risks, organizations can allocate resources efficiently and address the most critical challenges first, minimizing long-term damage to resilience. Embracing this approach enables practitioners to enhance overall supply chain resilience, guiding key management decisions for the development of sustainable and adaptive strategies.

Originality/value

This paper marks the first comprehensive attempt to assess supply chain 5.0 digitalization risks using decision-making methods like AHP and DEMATEL. The integrated approach contributes novel insights to the field of supply chain risk management, specifically aiding management decision-making in the face of digitalization challenges.

Purpose

This paper aims to analyze the role and performance of different artificial intelligence (AI) algorithms in forecasting future movements in the main indices of the world’s largest stock exchanges.

Design/methodology/approach

Drawing on finance-based theory, an empirical and experimental study was carried out using four AI-based models. The investigation comprised training, testing and analysis of model performance using accuracy metrics and F1-Score on data from 34 indices, using 9 technical indicators, descriptive statistics, Shapiro–Wilk, Student’s t and Mann–Whitney and Spearman correlation coefficient tests.

Findings

All AI-based models performed better than the markets' return expectations, thereby supporting financial, strategic and organizational decisions. The number of days used to calculate the technical indicators enabled the development of models with better performance. Those based on the random forest algorithm present better results than other AI algorithms, regardless of the performance metric adopted.

Research limitations/implications

The study expands knowledge on the topic and provides robust evidence on the role of AI in financial analysis and decision-making, as well as in predicting the movements of the largest stock exchanges in the world. This brings theoretical, strategic and managerial contributions, enabling the discussion of efficient market hypothesis (EMH) in a complex economic reality – in which the use of automation and application of AI has been expanded, opening new avenues of future investigation and the extensive use of technical analysis as support for decisions and machine learning.

Practical implications

The AI algorithms' flexibility to determine their parameters and the window for measuring and estimating technical indicators provide contextually adjusted models that can entail the best possible performance. This expands the informational and decision-making capacity of investors, managers, controllers, market analysts and other economic agents while emphasizing the role of AI algorithms in improving resource allocation in the financial and capital markets.

Originality/value

The originality and value of the research come from the methodology and systematic testing of the EMH through the main indices of the world’s largest stock exchanges – something still unprecedented despite being widely expected by scholars and the market.