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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 research aims to examine how decision-makers’ demographic traits affect the integration of foreign workforce into Tirolean (Austria) companies. With continuous world migrations, Tirol experiences a great inflow of foreign workforce. While integrating into the workforce, the foreign workers interact with various decision-makers whose demographic traits (e.g., age, gender, nationality) potentially influence the success of the integration process. To gather data on the integration levels of a foreign workforce, the author conducted a questionnaire. Furthermore, several statistical analyses were run to determine if the relationship between demographic characteristics and integration success exists. The study reveals that demographic characteristics influence decision-makers’ acceptance of expatriates, as well as their recruitment, integration, and training and development outcomes. The empirical results indicate the strength of relationships identified through analyses. The study is limited to geographical, as well as the scope of the sample size, as the data are obtained from Tirol only. In addition, the results from the study serve as a basis for future discussions and research.

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.

The rapid, yet low-profit, expansion of the production capacity of state-owned enterprises (SOEs) represents a remarkable phenomenon. However, the motivation behind this key operational decision remains underexplored, especially concerning the prioritization of sociopolitical and financial goals in operations management. Drawing on the multiple-goal model in the behavioral theory of the firm (BTOF), the authors' study aims to examine how SOE capacity expansion is driven by performance feedback regarding the sociopolitical goal of employment provision and how SOEs differently prioritize sociopolitical and financial goals based on negative versus positive feedback on the sociopolitical goal.

The authors' study uses panel data on 826 Chinese SOEs in manufacturing industries from 2011 to 2019. The authors employ the fixed-effects model with Driscoll–Kraay standard errors, which are robust to heteroscedasticity, autocorrelation and cross-sectional dependence.

The authors find that SOEs increase capacity expansion as sociopolitical feedback becomes more negative, but they may not increase capacity expansion in response to positive sociopolitical feedback. Moreover, negative profitability feedback strengthens SOEs' capacity expansion in response to negative sociopolitical feedback. In contrast, negative profitability feedback weakens their response to positive sociopolitical feedback.

The authors' study offers a novel behavioral explanation of SOEs' operational decisions regarding capacity expansion. While the literature has traditionally assumed multiple goals as either hierarchical or compatible, the authors extend the BTOF's multiple-goal model to illuminate when firms pursue sociopolitical and financial goals as compatible (i.e. the activation rule) versus hierarchical (i.e. the sequential rule), thereby reconciling their tension in distinct performance situations. Practically, the authors provide fine-grained insights into how operations managers can prioritize multiple goals when making operational decisions. The authors' study also shows how policymakers can influence SOE operations to pursue sociopolitical goals for public benefit.

Pharmaceutical supply chains (PSCs) need a well-operating and faultless logistics system to successfully store and distribute their medicines. Hospitals, health institutes, and pharmacies must maintain extra stock to respond requirements of the patients. Nevertheless, there is an inverse correlation between the level of medicine stock and logistics service level. The high stock level held by health institutions indicates that we have not sufficiently excellent logistics systems presently. As such, selecting appropriate logistics service providers (drug distributors) is crucial and strategic for PSCs. However, this is difficult for decision-makers, as highly complex situations and conflicting criteria influence such evaluation processes. So, a robust, applicable, and strong methodological frame is required to solve these decision-making problems.

To achieve this challenging issue, the authors develop and apply an integrated entropy-WASPAS methodology with Fermatean fuzzy sets for the first time in the literature. The evaluation process takes place in two stages, as in traditional multi-criteria problems. In the first stage, the importance levels of the criteria are determined by the FF-entropy method. Afterwards, the FF-WASPAS approach ranks the alternatives.

The feasibility of the proposed model is also supported by a case study where six companies are evaluated comprehensively regarding ten criteria. Herewith, total warehouse capacity, number of refrigerated vehicles, and personnel are the top three criteria that significantly influence the evaluation of pharmaceutical distribution and warehousing companies. Further, a comprehensive sensitivity analysis proves the robustness and effectiveness of the proposed approach.

The proposed multi-attribute decision model quantitatively aids managers in selecting logistics service providers considering imprecisions in the multi-criteria decision-making process.

A new model has been developed to present a sound mathematical model for selecting logistics service providers consisting of Fermatean fuzzy entropy and WASPAS methods. The paper's main contribution is presenting a comprehensive and more robust model for the ex ante evaluation and ranking of providers.

Human resources (HR) are undoubtedly one of the most important factors of any organization. That is why making decisions on the HR policy is becoming a very sensitive issue, both when hiring adequate candidates for the job and during the process work, i.e., during training and development of employees who work in the organization. The purpose of this study was to investigate the importance of HR and decisions on the HR policy as the premise for generating the organization’s expected business excellence. The starting assumption of this chapter is that appropriate decisions on the HR policy are predictors of engaging adequate employees and managing their potentials on the right way. The research is directed toward identifying differences in decisions on the personnel policy in organizations from Serbia that have different decision-makers and different management styles, which are directly related to their business success. Apart from the decision-maker, a significant role in profiling an organization’s personnel should also be done by the Human Resource Department, who take part in recruiting, selecting for education, building, and motivating personnel. In order to confirm the starting assumption, the comparative analysis method, the synthesis method, and the multiple comparison and statistical test methods are used.

Cyberattacks have become a major threat to small and medium-sized enterprises. Their prevention efforts often prioritize technical solutions over human factors, despite humans posing the greatest risk. This article highlights the importance of developing tailored behavioral interventions. Through qualitative interviews, we identified three persona types with different psychological biases that increase the risk of cyberattacks. These psychological biases are a basis for creating behavioral interventions to strengthen the human factor and, thus, prevent cyberattacks.

We conducted structured, in-depth interviews with 44 employees, decision makers and IT service providers from small and medium-sized Swiss enterprises to understand insecure cyber behavior.

A thematic analysis revealed that, while knowledge about cyber risks is available, no one assumes responsibility for employees’ and decision makers’ behavior. The interview results suggest three personas for employees and decision makers: experts, deportees and repressors. We have derived corresponding biases from these three persona types that help explain the interviewees’ insecure cyber behavior.

This study provides evidence that employees differ in their cognitive biases. This implies that tailored interventions are more effective than one-size-fits7-all interventions. It is inherent in the idea of tailored interventions that they depend on multiple factors, such as cultural, organizational or individual factors. However, even if the segments change somewhat, it is still very likely that there are subgroups of employees that differ in terms of their misleading cognitive biases and risk behavior.

This article discusses behavior directed recommendations for tailored interventions in small and medium-sized enterprises to minimize cyber risks.

The contribution of this study is that it is the first to use personas and cognitive biases to understand insecure cyber behavior, and to explain why small and medium-sized enterprises do not implement behavior-based cybersecurity best practices. The personas and biases provide starting points for future research and interventions in practice.

The paper aims to propose an innovative two-stage decision model to address the sustainable-resilient supplier selection and order allocation (SSOA) problem in the single-valued neutrosophic (SVN) environment.

First, the sustainable and resilient performances of suppliers are evaluated by the proposed integrated SVN-base-criterion method (BCM)-an acronym in Portuguese of interactive and multi-criteria decision-making (TODIM) method, with consideration of the uncertainty in the decision-making process. Then, a novel multi-objective optimization model is formulated, and the best sustainable-resilient order allocation solution is found using the U-NSGA-III algorithm and TOPSIS method. Finally, based on a real-life case in the automotive manufacturing industry, experiments are conducted to demonstrate the application of the proposed two-stage decision model.

The paper provides an effective decision tool for the SSOA process in an uncertain environment. The proposed SVN-BCM-TODIM approach can effectively handle the uncertainties from the decision-maker’s confidence degree and incomplete decision information and evaluate suppliers’ performance in different dimensions while avoiding the compensatory effect between criteria. Moreover, the proposed order allocation model proposes an original way to improve sustainable-resilient procurement values.

The paper provides a supplier selection process that can effectively integrate sustainability and resilience evaluation in an uncertain environment and develops a sustainable-resilient procurement optimization model.