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Business analytics (BA) is a new frontier of technology development and has enormous potential for value creation. Information systems research shows ample evidence of its positive business impacts and organizational performance. However, there is limited understanding of how decision-makers or users of BA outcomes actually engage with data analysts in the process of data-driven insight generation and how they improve their understanding of business environments using BA outcomes. To aid this engagement and understanding, this study investigates the interaction between decision-makers and data analysts when they attempt to uncover data capacities and business needs and acquire business insights from BA tools.

This study employs an interpretive field study with thematic analysis. The authors conducted interviews with 31 participants who all relied on BA in their daily decisions. The study participants were engaged in different BA roles, including data analysts and decision-makers. They validated the applicability and usefulness of our findings through a focus group with eight practitioners, including decision-makers and data analysts from the same companies.

This study proposes a process model of data-driven sensemaking and sensegiving based on Weick’s sensemaking framework. The findings exhibit that decision-makers are engaged in sensemaking by identifying areas of focus, determining BA scope, evaluating generated insights and turning BA into action. The findings also show that data analysts engage in sensemaking by consolidating data, data understanding, preparing preliminary outcomes and generating actionable reports. This study shows how sensemaking processes and sensegiving activities work together over time through immediate enactment, selection and decision cycles.

This study is a first attempt to understand interactions in the context of BA using the perspective of sensemaking and sensegiving.

How to embed artificial intelligence (AI) in human resource management (HRM) is one of the core challenges of digital HRM. Despite regulations demanding humans in the loop to ensure human oversight of AI-based decisions, it is still unknown how much decision-makers rely on information provided by AI and how this affects (personnel) selection quality.

This paper presents an experimental study using vignettes of dashboard prototypes to investigate the effect of AI on decision-makers’ overreliance in personnel selection, particularly the impact of decision-makers’ information search behavior on selection quality.

Our study revealed decision-makers’ tendency towards status quo bias when using an AI-based ranking system, meaning that they paid more attention to applicants that were ranked higher than those ranked lower. We identified three information search strategies that have different effects on selection quality: (1) homogeneous search coverage, (2) heterogeneous search coverage, and (3) no information search. The more applicants were searched equally often (i.e. homogeneous) as when certain applicants received more search views than others (i.e. heterogeneous) the higher the search intensity was, resulting in higher selection quality. No information search is characterized by low search intensity and low selection quality. Priming decision-makers towards carrying responsibility for their decisions or explaining potential AI shortcomings had no moderating effect on the relationship between search coverage and selection quality.

Our study highlights the presence of status quo bias in personnel selection given AI-based applicant rankings, emphasizing the danger that decision-makers over-rely on AI-based recommendations.

As the scale of mega transportation infrastructure projects (MTIs) continues to expand, the complexity of engineering construction sharply increases and decision-making sustainability faces severe challenges. Decision-making for mega transportation infrastructure projects unveils the knowledge-intensive characteristic, requiring collaborative decisions by cross-domain decision-makers. However, the exploration of heterogeneous knowledge fusion-driven decision-making problems is limited. This study aims to improve the deficiencies of existing decision-making by constructing a knowledge fusion-driven multi-attribute group decision model under fuzzy context to improve the sustainability of MTIs decision-making.

This study utilizes intuitionistic fuzzy information to handle uncertain information; calculates decision-makers and indicators weights by hesitation, fuzziness and intuitionistic fuzzy entropy; applies the intuitionistic fuzzy weighted averaging (IFWA) operator to fuse knowledge and uses consensus to measure the level of knowledge fusion. Finally, a calculation example is given to verify the rationality and effectiveness of the model.

This research finally constructs a two-level decision model driven by knowledge fusion, which alleviates the uncertainty and fuzziness of decision knowledge, promotes knowledge fusion among cross-domain decision-makers and can be effectively applied in practical applications.

This study provides an effective decision-making model for mega transportation infrastructure projects and guides policymakers.

This study aims to examine the impact of the decision makers’ risk preference on logistics routing problem, contributing to logistics behavior analysis and route integration optimization under uncertain environment. Due to the unexpected events and complex environment in modern logistics operations, the logistics process is full of uncertainty. Based on the chance function of satisfying the transportation time and cost requirements, this paper focuses on the fourth party logistics routing integrated optimization problem considering the chance preference of decision makers from the perspective of satisfaction.

This study used the quantitative method to investigate the relationship between route decision making and human behavior. The cumulative prospect theory is used to describe the loss, gain and utility function based on confidence levels. A mathematical model and an improved ant colony algorithm are employed to solve the problems. Numerical examples show the effectiveness of the proposed model and algorithm.

The study’s findings reveal that the dual-population improvement strategy enhances the algorithm’s global search capability and the improved algorithm can solve the risk model quickly, verifying the effectiveness of the improvement method. Moreover, the decision-maker is more sensitive to losses, and the utility obtained when considering decision-makers' risk attitudes is greater than that obtained when the decision-maker exhibits risk neutrality.

In an uncertain environment, the logistics decision maker’s risk preference directly affects decision making. Different parameter combinations in the proposed model could be set for decision-makers with different risk attitudes to fit their needs more accurately. This could help managers design effective transportation plans and improve service levels. In addition, the improved algorithm can solve the proposed problem quickly, stably and effectively, so as to help the decision maker to make the logistics path decision quickly according to the required confidence level.

Considering the uncertainty in logistics and the risk behavior of decision makers, this paper studies integrated routing problem from the perspective of opportunity preference. Based on the chance function of satisfying the transportation time and cost requirements, a fourth party logistics routing integrated optimization problem model considering the chance preference of decision makers is established. According to the characteristics of the problem, an improved dual-population ant colony algorithm is designed to solve the proposed model. Numerical examples show the effectiveness the proposed methods.

This study aims to assess National Health Service (NHS) decision-making protocols during the pandemic, with two primary objectives: (1) to establish whether decision-making protocols changed during the pandemic and (2) to evaluate if these changes could inform future decision-making strategies beyond the pandemic. By focusing on the shift from traditional to emergency decision-making processes, this research seeks to derive actionable insights for enhancing policy and practice in crisis conditions.

We employ a mixed-methods approach, gathering data through an online survey targeted at senior NHS decision-makers involved in the pandemic response. Our survey collected quantitative and qualitative data to assess changes in decision-making protocols. The analysis included statistical techniques to quantify changes and thematic analysis to explore their implications, providing a detailed understanding of decision-making adaptations during the crisis and their potential future impact.

Our findings clarify the role of the NHS values and constitution, which prioritize patient welfare, dignity and equitable access to healthcare, guiding all decision-making. During the pandemic, the urgency to respond swiftly necessitated modifications to these guiding principles. Traditional processes were adapted, allowing for more rapid decision-making while still aligning with the core values, effectively balancing immediate response needs with long-term healthcare commitments.

Our research contributes to decision-making under crisis conditions within a healthcare context and brings together a theoretical background which has accommodated the development of models and approaches that can be utilized by both service and manufacturing organizations. In addition, we have sought to bring together the importance of decision-making protocols under crisis conditions using observations from respondents who experienced decision-making at a senior level prior, during and beyond the period of the COVID-19 pandemic, which has assisted in the models developed in this paper. In addition, our empirical research demonstrates the importance that the values of the organization have upon decision-making and how such values need to be adjusted in the light of crisis operations.

Our research provides insightful observations relating to the pressures upon decision-making protocols under crisis conditions and provides senior decision-makers with an approach to realigning values to cope with unusual and highly pressurized operating environments. Notably, there is a clear requirement for decision-makers to communicate clearly to staff the need to temporarily alter the modus operandi to reflect crisis operations.

To the best of the authors’ knowledge, this is the first study to explore decision-making in the NHS during a pandemic and to clearly demonstrate how such decision-making needs to be adapted to reflect the nature and scope of delivering a complex healthcare service under crisis conditions.

This study aims to investigate the influence of two perceived organizational ethics systems (perceived ethics training quality and integrity-based climate) on the ethical decision-making (EDM) of tax accountants in Ghana. The study also examines the moderating role of the decision-makers’ financial situation on the quality ethics training–EDM relationship.

Survey data from 356 tax accountants were analyzed using the partial least squares structural equation modeling technique.

The results show that the two ethics systems influence EDM, but their extent of influence varies across the stages of EDM. Specifically, quality ethics training is a better predictor of EDM at the ethical issue recognition stage, whereas integrity-based climate is a better predictor of EDM at the ethical intention stage. The study also found that decision-makers’ financial situation predicts the ethical recognition stage of EDM but does not moderate the quality ethics training–EDM relationship.

This study recommends the concurrent deployment of quality ethics training and an integrity-based work climate to improve ethical behavior. Policymakers should also emphasize a work climate that promotes honesty, conscientiousness and ethical principles (integrity-based climate) to improve ethical intentions.

This study applied the interactionist theory by capturing the relative effects of two organizational ethics systems and an individual-level situational factor in a single model. To the best of the authors’ knowledge, this is the first study that tests the moderation effect of decision-makers’ financial situation on the ethics training–EDM relationship in a developing country context.

This research aims to investigate post-forest fire perceptions of businesses towards the environment as a stakeholder. Through interviews with affected businesses, the authors aim to understand whether disasters prompt sustainability prioritisation beyond legal or market demands, shedding light on potential shifts in environmental attitudes and decision-making processes.

The authors used qualitative methods to investigate post-disaster shifts in environmental perceptions. Using site visits, preparatory meetings and semi-structured interviews between October 2017 and April 2021, the authors gained insights into destruction, recovery efforts and stakeholder perspectives. Content analysis provided valuable decision-making insights, particularly in understanding the landscape dominated by SMEs reliant on short-term strategies.

Interviews revealed varied perspectives on stakeholder recognition, especially concerning the natural environment. Although some managers promptly acknowledged stakeholder groups, the recognition of the natural environment as one varied. Concerning the natural environment as a stakeholder, responses ranged from ecological acknowledgment to denying its stakeholder status. Despite differing views, many agreed on the forest's importance, especially for resource-reliant industries. The findings suggest that although many decision makers verbally acknowledge the natural environment as a stakeholder, their actions reveal the opposite.

The limitations are the COVID-19 pandemic in the data research phase. The methodology applied (qualitative) can be a limitation in itself and the authors recommend further research, applying mixed or quantitative methods. The research covers one event in one country. It is relevant to test our questions and conclusions in other countries/after other natural disasters. Incorporating other stakeholders' views and exploring alternative theories could enhance understanding and challenge existing results.

This study holds practical implications for understanding the relationship between organisations and the natural environment, particularly in recognising it as a stakeholder. By acknowledging the environment as a stakeholder, organisations can mitigate the effects of future natural disasters, as well as reducing their environmental footprints. Implementing these insights can lead to more informed decision-making processes and contribute to more effective resources and stakeholder management.

Recognizing the environment as a stakeholder fosters environmental consciousness and community engagement. Addressing the natural environment as such enhances the ownership and responsibility of the surrounding natural environment.

The study's originality lies in its exploration of organisational responses to natural disasters, particularly in recognizing the environment as a stakeholder. It offers unique insights into decision-making processes and attitudes towards environmental responsibility, contributing to advancing understanding and informing strategies for sustainable disaster management on a global scale.

This article presents a methodological application of decision support with the purpose of identifying and better aligning sustainable banking strategies. Those strategies are based on best practices declared by employees and conducted during efficient periods affecting sustainable production, the health quality of clients, the organization’s profitability and social impact on the local community across different sectors.

The approach involves a two-phase process: first, it employs directional data envelopment analysis (DEA) to benchmark knowledge based on employee opinions gathered through interviews to evaluate strategies related to banking services; then, using the best-worst method and ELECTRE outranking incorporating elements of fuzzy set theory based on an experienced decision-maker’s input, sustainable banking strategies are ranked according the different perspectives for leveraging outputs from the first step.

The outcomes yield a ranking of strategies, emphasizing the crucial role of technology in banking services while highlighting the need for more agile services to ensure customer satisfaction. This underscores the necessity of aligning with the market perspective, as fintech companies are reshaping the socio-technological-environmental landscape of financial services.

The research combined DEA and multicriteria analysis in the context of the banking sector, providing a comprehensive and analytically robust approach translated as a decision-making framework for promoting sustainability by aligning operational efficiency and social responsibility. These tools can guide banks in adopting more sustainable practices that benefit the institution, society and the environment.

Decisions in the banking sector encompass a wide array of concepts, from internal technical factors to customer feedback on service processes and offerings. The proposed approach considers decision analysis in complex environments, and the application developed in this study considered not only internal banking activity-oriented concepts but also the preferences of human agents developing them and the managerial perspective focused on issues involving components associated with sustainability.

By integrating DEA with multicriteria analysis, this study paves the way for a more efficient, environmentally conscious and socially responsible management scenario in the Brazilian banking sector. This research assesses operational efficiency and offers a comprehensive framework for selecting and implementing sustainable practices in the banking sector.

The purpose of this study is to analyze the impacts of the COVID-19 pandemic on the performance of companies using a hybrid Multi-Criteria Decision-Making (MCDM) approach. Specifically, the study examines Türkiye’s Top 500 Industrial Enterprises to analyze their performance before and during the pandemic, and to capture their performance in determining investment and production strategy.

To achieve the study’s objectives, the Fuzzy Best-Worst Method (F-BWM) was used to obtain importance levels of performance indicators, decreasing the vagueness in experts’ decision-making preferences. The Measurement Alternatives and Ranking According to Compromise Solution (MARCOS) method was used to rank enterprises based on their performance.

The COVID-19 pandemic has clearly had a substantial impact on the performance of Türkiye’s top 500 industrial enterprises. While some companies suffered decreased sales, others reported that their revenues increased or remained constant during the outbreak. The results reveal that the pandemic caused a shift in the initial ranking outcomes for the first two enterprises.

The study’s limitations include the sample size and the time period under consideration, which may have an impact on the generalizability of the findings.

Decision-makers’ investment, employment and operational decisions were influenced by the impact of the COVID-19 pandemic. The results provide insights for decision-makers on how to achieve higher growth and performance under the pressure of the pandemic.

The study’s practical consequences help decision-makers understand how to attain higher growth and performance in the face of the epidemic.

The originality of this study lies in using a hybrid MCDM approach to examine the impact of the COVID-19 pandemic on company performance. A hybrid MCDM approach is proposed to help decision-makers make the best possible investment and implementation decisions.

This paper aims to explore the underlying intention behind using blockchain technology (BLCT) in the agri-food supply chain (AFSC). This is achieved by using a conceptual framework based on technology acceptance models that considers various factors influencing user behavior toward implementing this technology in their practices.

The conceptual framework developed is empirically validated using structural equation modeling (SEM). A total of 258 respondents from agri-food domain in India were involved in this survey, and their responses were analyzed through SEM to validate our conceptual framework.

The findings state that food safety and security, traceability, transparency and cost highly influence the intention to use BLCT. Decision-makers of the AFSCs are more inclined to embrace BLCT if they perceive the usefulness of the technology as valuable and believe it will enhance their productivity.

This study contributes to the existing literature by providing thorough examination of the variables that influence the intention to adopt BLCT within the AFSC. The insights aim to benefit industry decision-makers, supply chain practitioners and policymakers in their decision-making processes regarding BLCT adoption in the AFSC.

This study investigates how decision-makers’ perceptions of BLCT influence their intention to use it in AFSCs, as well as the impact of the different underlying factors deemed valuable in the adoption process of this technology.