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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.

This article seeks to provide a theoretical framework for facilitating talent management decisions in the music industry.

Strategic decision‐making theory and the resource‐based view of strategy are used to identify the talent‐selection process as a core capability in the entertainment industry. Their original combination leads to the introduction of a framework aimed at facilitating the selection and development of core competencies and capabilities in music companies, and thus at increasing their likelihood of creating and sustaining a competitive advantage based on their artist selection processes.

The integration of both theories in the music sector leads to the need for the organisation's ability to “improvise” and develop “skilled decision makers”. The industry is identified as an atypical high velocity environment, in which incremental approaches may not be sufficient to adopt by managers, because artist investments usually represent long‐term commitments for the firm. Three different existing types of talent valuation techniques are identified, which can be facilitated by complementing resource‐based and decision‐making perspectives.

The paper does not discuss differences in the types of music organisations such as publishers, record companies, labels etc. Moreover, it focuses on popular music in general only. Empirical testing of the proposed findings is needed to further validate the capability framework.

The framework provides a managerial guideline for implementing decision models in the music industry and increasing the success rate of artist selection.

The paper uses the specific context of the music industry to introduce a methodology of how organisational decision processes may eventually lead to a sustainable competitive advantage. It provides a starting point for linking resource‐based and strategic decision‐making theory, since it indicates how decision models should be developed from a core capability perspective.

Fierce competition in the airline industry compels airline companies to offer various services. Yet, while companies strive to become preferable, customers confront numerous airline selection indicators, and as such causes vagueness in human thinking that needs to be systematically and normatively resolved. Accordingly, the purpose of this paper is initially to establish a strategic decision model that incorporates key selection indicators, among hundreds of criteria, through a systematic approach. Subsequently, it also aims to investigate the relative importance of these indicators for passengers.

This research first utilises a comprehensive literature review to uncover key indicators used in airline selection. Afterwards, the outcome obtained from the first phase initiated the phase of determining the prioritisation of these key selection indicators, through the analytic hierarchy process (AHP) method, based on passengers’ judgments.

The outcome of structuring a strategic decision model reveals 32 key selection indicators to be mainly considered by passengers and these indicators are grouped under five dimensions in this paper. Then, the prioritisation results given by the AHP indicate that “price-related factors” and “customer satisfaction-related factors”, respectively, are more important dimensions for passengers while selecting the best airline company.

The proposed approach provides a novel way to identify and prioritise key airline selection indicators for different passengers, through using the AHP, as a response to the need of adopting a systematic and comprehensive manner with the inclusion of general industry norms. Within this scope, the established model and the prioritisation results can be used as a reference by both airline passengers during their decision-making processes and airline companies which aim for becoming more competitive.

This study aims to provide a practical and novel solution for the complex multi-criteria decision-making (MCDM) problem of airline route selection, which is characterized by conflicting criteria, alternative routes, and complex judgments.

This study proposes a hybrid MCDM approach using Interval-valued Pythagorean Fuzzy AHP and Interval-valued Pythagorean Fuzzy weighted aggregated sum product assessment (WASPAS) methods. Decision analysis is applied to select a new route between different alternatives through selection criteria. Pythagorean Fuzzy AHP is used for weighting criteria, and Pythagorean Fuzzy WASPAS is used for assessing alternatives. The pair-wise linguistic comparisons of selection criteria are transferred into Pythagorean fuzzy numbers (PFNs) to weigh each criterion’s importance.

The pair-wise linguistic comparisons of selection criteria are transferred into PFNs to weigh each criterion’s importance. The results of these comparisons show that the main criteria, cost (43% weight) and demand (33% weight), impact route selection decisions more than social/economic conditions (15% weight) and competitiveness (9% weight). Regarding the criteria, the five routes alternative were evaluated by the route development experts, and the best route was selected with Pythagorean Fuzzy WASPAS.

The proposed model is used for a route selection problem of Turkish Airlines, the airline that flies to the most countries in the world.

To the best of the authors’ knowledge, this study is the first to use the Interval-valued Pythagorean Fuzzy AHP combined with Interval-valued Pythagorean Fuzzy WASPAS to solve the route selection problem. This hybrid MCDM methodology presents a novel and feasible solution for selecting the new route for airlines.

The purpose of this paper is to explore how firms that do both sourcing and selling choose which countries to source from and to which countries to sell. It also looked at the role of competitive strategy, vertical integration, and foreign involvement in the decision‐making.

A survey instrument that was designed based on personal interviews with 20 German garment industry executives was administered to 750 German, Austrian and Swiss garment manufacturers. In total, 93 questionnaires were usable. Factor analysis was employed in evaluating the data.

The results indicate that firms that both source and sell at the same time have more complex decision making than normative models suggest. These decisions tend not to be isolated decisions. The factors that are considered in sourcing are different in the decision as to where to sell. Foreign involvement, competitive strategy, and vertical integration influence the firms' decision making.

The study is exploratory in nature and it is limited in its application. Moreover, the disproportionately large number of German respondents may skew the results. In addition, the total number of respondents is relatively small. The study may also suffer from any or all of the following deficiencies: lack of reliability and validity test, having only one executive per firm respond to the survey, not considering country‐specific characteristics, and lack of control for the market‐entry strategy and market size variables.

Most research in this area focuses on either the sourcing or the selling side. This study looks at how sourcing and selling decisions are made by firms engaged in both activities. Additionally, the roles of competitive strategy, vertical integration, and foreign involvement in relation to the decision making are investigated.

The purpose of this paper is to develop a decision tool to help managers make more informed decisions regarding their investments in advanced manufacturing technologies.

Selection of a new technology is multidimensional in nature and interdependent relationships exist among various elements of the system. In addition, both quantitative and qualitative factors need to be considered in the evaluation process. The Analytic Network Process (ANP) methodology satisfies these requirements by considering interdependencies among all the factors and by allowing transformation of qualitative judgments into quantitative values for the decision analysis. These capabilities of ANP make it a perfect methodology for use in the development of our decision tool. Once technology alternatives and selection criteria are identified by the decision maker, several pair‐wise comparisons are conducted to determine local priorities for the selection criteria and alternatives. These priorities are then used to determine the overall priorities for the alternatives. The technology alternative with the highest overall priority is chosen for adoption.

Allowing for interdependencies among selection criteria, as well as between alternatives and selection criteria, provides a more realistic evaluation process than other selection processes that ignore such interdependencies.

The model provides decision makers a tool for evaluating several competitive technology alternatives and selecting the most appropriate technology for adoption.

The paper discusses the inclusion of the subjective judgments of the decision maker in the evaluation process and use of ANP methodology for transforming these judgments into quantitative values for rankings of the alternatives.

Quantitative objective studies on the problem of celebrity selection are lacking. Furthermore, existing research does not recognize the group decision-making nature and the possibility of customer involvement in celebrity or influencer selection for social media marketing. This study conceptualizes celebrity selection as a multi-attribute group decision-making problem while deriving the final ranking of celebrities/influencers using interactive and flexible criteria based on the value tradeoff approach. The article thus proposes and demonstrates a quantitative objective method of celebrity selection for a brand or campaign in an interactive manner incorporating customer's preferences as well.

Each decision-maker's preferences for celebrity selection criteria are objectively captured and converted into an overall group preference using a modified generalized fuzzy evaluation method (MGFEM). The final ranking of celebrities is then derived from an interactive and criteria-based value tradeoff approach using the flexible and interactive tradeoff method.

The approach gives a different ranking of celebrities for two campaigns based on group members' perceived importance of the selection criteria in different scenarios. This group includes decision-makers (DMs) from the brand, marketing communication agency and brand's customers. Further, each group member has an almost equal say in the decision-making based on fuzzy evaluation and an interactive and flexible value tradeoff approach to celebrity selection for receiving a rank order.

The approach uses secondary data on celebrities and hypothetical scenarios. Comparison with other methods is difficult, as no other study proposes a multi-criteria group decision-making approach to celebrity selection especially in a social media context.

This approach can help DMs make more informed, objective and effective decisions on celebrity selection for their brands or campaigns. It recognizes that there are multiple stakeholders, including the end customers, each of whose views is objectively considered in the aspects of group decision-making through a fuzzy evaluation method. Further, this study provides a selection mechanism for a given context of endorsement by objectively and interactively encapsulating stakeholder preferences.

This robust and holistic approach to celebrity selection can help DMs objectively make consensual decisions with partial or complete information. This quantitative approach contributes to the literature on selection mechanisms of influencers, celebrities, social media opinion leaders etc. by providing a methodological aid that encompasses aspects of interactive group decision-making for a given context. Moreover, this method is useful to DMs and stakeholders in understanding and incorporating the effect of nature or context of the brand and the campaign type in the selection of a celebrity or an influencer.

Robot selection is one of the critical decision-making tasks frequently performed by various industries in order to choose the best suited robot for specific industrial purposes. In recent marketplace, the number of robot manufacturers has increased remarkably offering a wide range of models and specifications; thus, robot selection has become indeed confusing as well as complicated task. Selection of an appropriate robot is a sensitive process; it may result massive letdown, if not chosen properly. Therefore, for unravel the selection problem; the purpose of this paper is to explore the preference ranking organization method for enrichment evaluation (PROMETHEE) II method.

Apart from a large variety of robotic systems, existence of various multi-criteria decision making (MCDM) tools and techniques may create confusion to the decision makers’ in regards of application feasibility as well as superiority in performance to work under different decision-making situations. In this context, the PROMETHEE II method has been found as an efficient decision-making tool which provides complete ranking order of all available alternatives prudently, thus avoiding errors in decision making.

In this context, the present paper highlights application potential of aforesaid PROMETHEE II method in relation to robot selection problem subjected to a set of quantitative (objective) evaluation data collected from the available literature resources. Advantages and disadvantages of PROMETHEE II method have also been reported in comparison to other existing MCDM approaches.

The study bears significant managerial implications. Proper evaluation and selection of appropriate candidate robot would be helpful for the industries in order to improve product quality as well as to increase productivity. Proper utilization of resources could be ensured. Functioning would be accurate with reduced timespan. As a consequence, company can increase its profit margin in long run.

The purpose of this paper is to change the traditional supplier selection methods by shifting the emphasis from using a single model to using multiple models in the unstructured decision‐making context and to provide a tool for decision makers to make informed decisions of supplier selection in the multiple perspectives.

There are various supplier selection models available in the literature. However, using the result of a single model as a basis for making the final decision could lead to a biased decision given the fact that any model has its limitations. The qualities of the decision‐making process and the decision itself increase by applying a multiple perspectives approach rather than a single model. The multiple perspectives decision‐making allows collaboration and knowledge sharing among the participants which leads to a less‐biased decision. This study examines commonly applied supplier selection models, formulates general perspectives of these models, and proposes a framework of multiple perspectives decision making for supplier selection. It further provides a structure of supplier selection system based on the proposed approach. Through a prototype of web portal, the study demonstrates the usefulness of the proposed multiple perspective system approach in the decision context of collaboration and knowledge sharing.

The general finding from this study is that the multiple perspectives approach to supplier selection enables the decision makers to actively participate and fully understand the decision‐making process through knowledge sharing which in turn ensures high quality of the final decisions.

Supplier selection decision makers can make more informed decisions through collaboration among all decision‐making participants in the multiple perspectives. It informs supply chain managers of the potentially positive effect of knowledge sharing on the decision‐making process in supplier selection.

Multiple perspectives decision making provides a novel approach that emphasizes on the knowledge sharing and collaboration between the experts, who are familiar with the supplier relations, and the decision makers who are responsible for the final decisions.