Search results

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.

The purpose of this paper is to investigate how individual interviewers’ dispositional cognitive motivations may influence interview interactions and outcomes. More specifically, this study explores the influence of the need for cognition, need for cognitive closure, and accountability on the relationship between first impressions and selection decisions.

In total, 41 graduate students were assigned the role of interviewers and were tasked to interview 331 undergraduate students at a large Midwestern university. The selection interview was designed to recruit qualified undergraduate students to the MBA program of the university.

First impressions significantly influenced selection decisions, but did not influence interviewers’ behaviors. Moreover, multilevel analyses reveal that interviewers’ need for cognition and accountability moderate the relationship between first impression and selection decisions, albeit in different direction. Need for cognition strengthens, whereas accountability weakens the relationship between first impression and selection decision.

A potential interviewer bias is apparent, where interviewers high on need for cognition tend to weight first impressions more in the decision process. However, this bias was not directly observable, since interviewers’ behaviors during the interview were not affected by first impressions.

The present study goes beyond previous research on first impressions in the employment interview, finding that dispositional differences account for the tendency to weigh first impressions in the selection decision.

Robot selection is basically a task of choosing appropriate robot among available alternatives with respect to some evaluation criteria. The task becomes much more complicated since apart from objective criteria a number of subjective criteria need to be evaluated simultaneously. Plenty of decision support systems have been well documented in existing literature which considers either objective or subjective data set; however, decision support module with simultaneous consideration of objective as well as subjective data has rarely been attempted before. The paper aims to discuss these issues.

Motivated by this, present work exhibits application potential of preference ranking organization method for enrichment evaluations (extended to operate under fuzzy environment) to solve decision-making problems which encounter both objective as well as subjective evaluation data.

An empirical case study has been demonstrated in the context of robot selection problem. Finally, a sensitivity analysis has been performed to make the robot selection process more robust. A trade-off between objective criteria measure and subjective criteria measure has been shown using sensitivity analysis.

Robot selection has long been viewed as an important decision-making scenario in the industrial context. Appropriate robot selection helps in enhancing value of the product and thereby, results in increased profitability for the manufacturing industries. The proposed decision support system considering simultaneous exploration of subjective as well as objective database is rarely attempted before.

The purpose of this paper is to propose a theoretical model that integrates various dimensional factors which influence decision-making process of class selection and enrolment, analysing different angles of this process and explaining those factors which determine students’ decision.

This study uses quantitative design to determine and explore students’ decision making in class selection and enrolment. There were 396 students who participated in this study. The data were analysed using principle component analysis to determine the dominant factor for class selection and enrolment.

The study has analysed different factors that can influence students’ decision for class selection and enrolment. Five important underlying factors have been identified which includes the class and lecturer factor, time-space factor, ease and comfort factor, course mate factor and commitment factor. Moreover, the Kruskal–Wallis test shows that there is a significant mean difference in choice and selection behaviour between genders and students’ personal attitudes.

This study is an early attempt to explore the wide fields of decision making in class selection and enrolment. It is hoped that follow-up studies would provide more coverage relative to the findings of this research.

One particular dimension of micro decision making faced by students is class (course) selection in the beginning of every academic semester/term. Class selection is very critical decision for students as it would reflect students expected outcome for their future career directions. More importantly, the decision made by the students may also affect their academic performance throughout their study.

From the perspective of the university’s administrators, this issue is very critical for planning purposes. Understanding the students’ behaviour in class selection could improve the cost effectiveness as well as the scheduling of course offering to enhance students’ and instructors’ teaching and learning experience.

While many studies try to explore the questions of what makes a student choose a specific college/university or a specific field, limited number have investigated the behaviour of students in class selection and enrolment. This paper contributes to bridging that gap.

The authors perform a large‐scale review of debiasing literature with the purpose of deriving a mutually exclusive and exhaustive debiasing taxonomy. This taxonomy is used to conceptualize debiasing activities in the supplier selection process. For each supplier selection‐debiasing construct, scale items are proposed.

A systematic classification approach was used to build a debiasing taxonomy, combined with a Q‐methodology.

Based on the developed and externally validated debiasing taxonomy, five debiasing activities for the supplier selection context are derived. The conceptual investigation of these supplier selection‐oriented debiasing measures helps both researchers and supply managers to gain a better understanding of debiasing mechanisms and to effectively further improve the supplier selection process by integrating behavioral aspects.

This research extends the taxonomy of decision biases developed by Carter, Kaufmann, and Michel, by systematically analyzing strategies to debias the decision‐making process. The highly fragmented research landscape on debiasing was inventoried and structured. As a result, a debiasing taxonomy was created that extracted five main debiasing categories. These were then conceptualized within the context of the supplier selection process. In doing so, debiasing literature from different research streams such as economics, psychology, and behavioral and strategic decision making was systematically integrated into the field of supply management. Proposed scale items allow for empirical investigation as a next step in the development of the nascent field of behavioral supply management.

The purpose of this paper is to create knowledge on the intuitive global sourcing process applied by small and medium-sized enterprise (SME) managers.

This study reports on qualitative inquiries with experienced sourcing managers from 10 SMEs in the textile industry. The study follows a three-step semi-structured interviewing process, allowing us to gradually unveil the detailed nature of the intuitive supplier selection process.

Nine of the 10 SMEs rely on a highly intuitive supplier selections process, where one supplier at a time is gradually taken into the exchange while testing the supplier’s behavior. The process consists of an early heuristics sub-process, which gradually switches over to a more advanced intuiting behavioral pattern-matching process.

Most OM/SCM research has treated global sourcing and supplier selection as a highly rational, analytical and deliberate optimization problem. This study uncovers a completely different, and frequently successful, intuitive process, which could inspire managers in companies of all sizes, faced with high uncertainty about global supplier selection decisions.

Intuition has recently been adopted in the global sourcing literature. However, this study is the first to offer detailed insights into a predominantly intuitive global sourcing process, specifically as it is managed by SMEs.

The purpose of this paper is to identify the radio frequency identification (RFID) strategic value attributes (RFIDSVAs) mechanism selections preferences and also integration of RFID tags with technology coordination tools (IRTWTCTs) alternatives ranking performance decisions in supply chain management (SCM). RFID-enabled techno-economic feasibility decisions are enhancing the SC visibility in apparel supply chains (ASCs). The RFIDSVAs mechanism selections have performed significant agility to strategic competitive advantages, namely, inventory visibility, multi-tags ownership transfer within trusted third party, etc.

Fuzzy analytical hierarchy process (FAHP) and FAHP-fuzzy Technique for Order of Preference by Similarity to Ideal Solution (FTOPSIS) approaches have been used to evaluate the quantitative assessment of RFIDSVA mechanisms selection decision based on weight priority orders and IRTWTCTs alternatives selection in ASC networks. The comparison of FAHP and FAHP-FTOPSIS approaches to evaluate the integrated framework develop in RFIDSVAs mechanisms and IRTWTCTs alternatives selection decisions in Indian multi-tier ASC networks.

The result found that the FAHP-FTOPSIS approaches have used to prioritizing the RFIDSVA mechanism selection weights and also identify the IRTWTCTs alternatives ranking preferences order in apparel SCM. The comparison between the FAHP and FAHP-FTOPSIS approach to quantitative assessments from RFIDSVA mechanisms and IRTWTCTs alternatives selection decisions, which enable them SC agility potential across multi-tier visibility in ASC networks. ASC stakeholders can be benefited by techno-economic feasibility decisions, RFID-enabled shop floor activities, multi-tags ownerships transfer in SCs and knowledge-based cryptography tags/items separation in SCs.

The research work has considered only five RFIDSVA mechanisms and also three integration of RFIDTWTCTs alternatives in multi-tier ASC. The strategic competitive advantages are achieved by RFID-enabled break-even tags price decisions and also techno-economic feasibility decision by contractual design multi-tier SC stakeholder’s involvements.

The pilot project study explores that the quantitative assessment decision has based on RFID-enable techno-economic feasibility in ASCs. Stakeholders can be benefited by inventory control of the financial losses, reducing the inventory inaccuracies and multi-tags ownership transfer within trusted third-party traceability in ASC networks.

This study explores the RFID-enabled apparel SC process and activities visibility (natural fibre’s fibre producer, fibre dyeing producer, yarn spinning producer, knitting and finishing producer).

The purpose of the paper is to propose a novel approach, based on grey clustering decision, to fill in an omission of quantitative monitoring parameter selection methods.

The basic monitoring parameter selection criteria and the corresponding calculation methods are presented. Then, the grey clustering decision model for monitoring parameter optimization selection is constructed, and an integrated weight determination method based on analytic hierarchy process (AHP) and information entropy is provided.

Basic principle for monitoring parameter selection is proposed and quantitative description is carried out for selection principle in engineering application. Grey clustering decision‐making model for monitoring parameter optimization selection is established. Comprehensive weight ascertainment method based on AHP and information entropy is provided to make the index weight more scientific.

At system design stage, it is of significance to carry out selection and optimization of monitoring parameters. After the optimization of monitoring parameters is confirmed, measurability analysis and design in parallel are carried out for convenience of timely information feedback and system design revision. Therefore, the system integration efficiency is improved and the cost of research and manufacturing is reduced.

Monitoring parameter optimization selection process based on grey clustering decision‐making model is described and the analysis result shows that the proposed method has certain degree of effectiveness, rationality and universality.

Service supplier selection is a multi-criteria decision-making (MCDM) problem assuming a strategic role for the competitiveness of high-tech manufacturing companies. Nevertheless, especially for service quality evaluation, there is little empirical evidence of the practical usefulness of MCDM methodologies. Aiming to cover this gap between theoretical approaches and empirical applications, the purpose of this paper is to propose a fuzzy extended analytic hierarchy process (FEAHP) approach for service supplier evaluation.

A hybrid approach which combines some of the strengths of the analytic hierarchy process (AHP) and of the fuzzy set theory is presented, as organized into five steps. A case study is used to evaluate the applicability in a real company context.

The usability of the approach is demonstrated in an aerospace company for solving the supplier selection problem of a business software whose applications are still in infancy: a Test Data Management System (TDMS). The illustrative application contains both “general” criteria to be used for other service supplier selection contexts as well as service-specific criteria related to software selection.

Even if the application regards the selection of a software supplier, the methodology can be generically extended to other services’ selection in complex manufacturing industries through the personalization of some criteria.

Implications can be derived both for business managers involved into the decision-making process and for suppliers identifying the most promising features of software quality.

The originality consists in the combination into a hybrid approach of the strong points of the AHP with the fuzzy set; the inclusion of multiple perspectives of decision criteria for service supplier selection, basically the “software product” and “supplier” ones; a real empirical application to test and demonstrate the efficacy and the practical utility of the proposed approach.

We investigate whether/how various kinds of experience predict managers' selection outcomes.

Drawing from personnel selection and decision-making research, we used a multilevel model to examine whether various types of experience predict employee selection outcomes in the National Basketball Association. We examined 289 selection events of basketball players by 63 general managers. Measures of general manager experience included tenure, education level, family relations, experience as a college coach, experience as a National Basketball Association Player, experience as a National Basketball Association scout and experience as a National Basketball Association coach.

College coaching experience, tenure and vicarious experience through family relations improved selection decision outcomes, while experience as a basketball player reduced the quality of selection decision outcomes. Tenure was associated with general managers’ ability to select employees who made high individual contribution to team wins; vicarious experience improved ability to select players with high peak individual performance, and college coaching experience improved general managers’ ability to select employees with higher contributions to team wins, higher peak individual performances and higher average performances over the course of players’ careers.

Experience’s importance is taken for granted, but this paper demonstrates that all experience is not equally effective. Notably, experience as a professional athlete did not seem to make managers better decision-makers about personnel than people who did not have that experience.

The purpose of this paper is to understand the reasons behind the intent for information technology (IT) outsourcing in academic institutions in GCC countries. It also aims to develop a multi-criteria decision model (MCDM) to aid the critical decision of IT outsourcing vendor selection.

The research utilizes a questionnaire-based survey to investigate reasons of IT outsourcing intent and the factors considered important for IT outsourcing vendor selection in academic institutions. The results of questionnaire-based study were utilized to develop a grey theory-based MCDM for vendor selection.

The results show that facilitating access to new technology, focus on core-competence, saving staff costs, and improved customer service are the most important factors for IT outsourcing intent while reputation of vendor, access to the state of art technology, quality of service, and knowledge of industry were considered as the most important factors for IT outsourcing vendor selection. Grey theory-based decision model was applied to a real case to facilitate the decision of selection of an IT outsourcing vendor.

Academic institutions that plan to outsource IT in future would be the major beneficiaries of this study. They can utilize the multi-criteria model to select the best vendor. The model facilitates a more rational decision making as it incorporates several criteria considered important for IT outsourcing vendor selection.

This study contributes to the body of research on IT outsourcing. It is first of its kind with its focus on academic institutions in GCC countries where currently education is a priority and IT is a backbone for its delivery. Another novelty of this research is that it propose a MCDM for IT outsourcing vendor selection. The findings of this study would serve as a guide to those institutions that intend to outsource IT functions to meet the ever growing needs of managing IT effectively.

The paper seeks to provide academic researchers and practitioners with a better understanding about purchasing strategies through an integrated approach to supplier selection and purchasing decisions.

This paper views supplier selection as a multi‐criteria problem. Through the analytical hierarchy process (AHP), in consideration of both quantitative and qualitative criteria, a set of candidate suppliers is identified. A multi‐objective linear programming (MOLP) model, with multiple objectives and a set of system constraints, is then formulated and solved to allocate the optimum order quantities to the candidate suppliers.

The paper provides tradeoffs among different objectives, which are more consistent with the complexity and nature of the real‐world decision‐making environment. It also offers better information and solutions supporting effective purchasing decisions.

The main concept of the proposed approach can be applicable to any organization with a purchasing function. However, its implementation will be very specific to a particular organization of interest, as each individual organization must define its own subjective criteria and constraints. The area of decision support system development, which automates (or computerizes) the input process of the proposed models and integrates with other databases in a company, will provide great opportunities for future research.

The paper provides practitioners with flexibility and effectiveness in their supplier selection and purchasing decision process and with a better understanding about their future purchasing strategies. The results from the application of the proposed models to the supplier selection problem at a high‐technology firm in Taiwan show that the models are effective and applicable.

This paper takes an integrated approach to problem analysis (i.e. multi‐objectives with both quantitative and qualitative information), uses a sound scientific methodology in model development (i.e. integrating AHP with MOLP), and provides practical use of the models. It offers additional knowledge and value to both academics and practitioners.

The waste-to-energy (WtE) project plays a significant role in the sustainable development of urban environments. However, the inherent “Not in my backyard” (NIMBY) effect presents substantial challenges to site selection decisions. While effective public participation is recognized as a potential solution, research on incorporating it into site selection decision-making frameworks remains limited. This paper aims to establish a multi-attribute group decision-making framework for WtE project site selection that considers public participation to enhance public satisfaction and ensure project success.

Firstly, based on consideration of public demand, a WtE project site selection decision indicator system was constructed from five dimensions: natural, economic, social, environmental and other supporting conditions. Next, the Combination Ordered Weighted Averaging (C-OWA) operator and game theory were applied to integrate the indicator weight preferences of experts and the public. Additionally, an interactive, dynamic decision-making mechanism was established to address the heterogeneity among decision-making groups and determine decision-maker weights. Finally, in an intuitive fuzzy environment, an “acronym in Portuguese of interactive and multi-criteria decision-making” (TODIM) method was used to aggregate decision information and evaluate the pros and cons of different options.

This study develops a four-stage multi-attribute group decision-making framework that incorporates public participation and has been successfully applied in a case study. The results demonstrate that the framework effectively handles complex decision-making scenarios involving public participation and ranks potential WtE project sites. It can promote the integration of expert and public decision-making preferences in the site selection of WtE projects to improve the effectiveness of decision-making. In addition, sensitivity and comparative analyses confirm the framework’s feasibility and scientificity.

This paper provides a new research perspective for the WtE project site selection decision-making, which is beneficial for public participation to play a positive role in decision-making. It also offers a valuable reference for managers seeking to effectively implement public participation mechanisms.

Contrary to the prevalent theory approaches that treat market selection and market entry mode selection as two related but essentially separate decisions, this paper argues that these should most appropriately be looked on as two aspects of one decision process. It proposes that an exhaustive list of factors that can influence outcomes of such an integrated process be developed and argues that an inclusive spectrum of analysis would be able to accommodate all business contexts and most relevant business practice. It then presents a new market and market entry mode selection model (MEMS) which conforms to the proposed inclusive spectrum of the underlying decision process analysis.

The purpose of this paper is to integrate the “triple bottom line (3BL)” approach in the supplier selection decision. It also aims to consider the feedback effect of the decision on strategic factors that determine the future viability of the firm in the market.

A multi-criteria decision model is developed that considers simultaneously the impact of three dimensions of 3BL approach and their sub-dimensions on the supplier selection decision. The proposed model is evaluated for a large white goods manufacturer using the analytic network process (ANP) approach.

The ANP considers the impact of variables, sub-variables, and their interdependencies simultaneously. The outcome of the model is the relative priorities for the firms considered as potential suppliers.

This research was conducted in one of the largest developing economies. The impact of integrating sustainability would be widespread due to the huge market in which the company operates. The results of this research can provide support to the decision makers in arriving at an optimal decision considering all sustainability dimensions.

The novelty of the approach lies in the application of multi-criteria model integrating sustainability dimensions with a feedback effect for supplier selection. The case company would benefit by showing its commitment toward environment and social responsibility leading to improved brand image and sustainable business.

While studies have established the important role of change agents throughout organizational change programs and emphasized the change agents’ impact on outcomes of change efforts, it could be argued that the decision-making procedure for the selection of change agents may also influence the success of organizational change programs in several ways. This research aims to explore and compare the potential influence of a traditional change agent selection procedure with a more systematic and objective approach.

A qualitative case study has been conducted, before and after testing a group decision support system for the selection of change agents in an organization that was on the verge of a major rebranding.

The main findings suggest the importance of objectivity, transparency and attention to competency in the selection procedure for the role, while providing evidence for using a systematic participative decision-support process for the selection of change agents, leading to several desirable organizational outcomes, including improvements in perception of objectivity, trust, transparency, fairness and competency related to the selection process.

Drawing from the previous studies and the presented findings, this study offers evidence on the importance of paying attention to the selection procedure for the change agent role as it could potentially have an influence on the employees across organizations and, consequently, the success of change programs.

The purpose of this paper is to categorize transportation choice research (mode choice and carrier selection) leading to insight on themes in the literature and directions for future research

The proposed transportation choice research categorization framework is based on a comprehensive literature review of the peer‐reviewed journal papers published over the past 20 years, supplemented with a review of practitioner articles to identify current challenges in the logistics field. The academic papers are analyzed in terms of research purpose/question, methodology, findings, and challenges addressed.

The review reveals that several important themes are under‐represented in the transportation choice literature: environmental and energy use concerns; security in the supply chain; supply chain integration; international growth; and the role of the internet and emerging information technologies. This review also found that simulation, case study, and interview methodologies are under‐represented, and that normative modeling research is only lightly represented in this research.

The contributions of this research are three‐fold: the development of a classification scheme for transportation choice research, a structured review that provides a guide to earlier research on the subject of transportation choice, and the identification of research issues for future investigation.

This research focuses on suggesting an optimized model for selecting best employees using advanced multi-criteria decision making method to a supply chain firm, who is planning to start a new cold chain business vertical.

Study has been conducted in a supply chain firm in North India, who wants to expand its business with the help of efficient team members. In total 38 applicants were considered for the study, as selected by the firm after initial screening from pool of talent. AHP-LP and TOPSIS-LP integrated approach were applied separately for evaluation and implementation of personnel selection model. Further, both the approaches were compared to find the best fit and optimized model.

As per the findings, both AHP and TOPSIS can be used to select the best candidate among the alternatives available. TOPSIS was found easier to implement as it involves ranking of applicants with respect to each skills required for respective job profile only once, whereas AHP involves pair-wise comparison among candidates with respect to each skills required for respective job profile and normalization of each comparison, resulting in the formation of number of comparison matrices. However, AHP is more reliable as it considers consistency check for each level of pair-wise comparison. Hence, there is a chance to avoid or revise the human judgment error. Integrated ranking and optimization approach minimizes the cost by suggesting the relevant positions to be filed to make an efficient team.

Group of interviewers are involved in the decision-making process, hence there are chances of biasness in ranking method which can influence the group decision. Research is limited to a particular geography of North India therefore needs to be tested for other regions also in order to generalize. The research will help the third party logistics (3PL) and other related firms in efficient team selection.

The researcher focuses on formalizing a method for potential candidate selection by considering the constraints of the organization. It has been observed that limited researches have been done on the application of AHP-LP or TOPSIS-LP integrated approach for selection process. Hence, this research proposes two integrated ranking-optimization method and suggests the best fit by comparing both the approaches.

The decision-making for additive manufacturing (AM) process selection is typically applied in the end of the product design stages based upon an already finished design. However, due to unique characteristics of AM processes, the part needs to be designed for the specific AM process. This requires potentially feasible AM techniques to be identified in early design stages. This paper aims to develop such a decision-making methodology that can seamlessly be integrated in the product design stages to facilitate AM process selection and assist product/part design.

The decision-making methodology consists of four elements, namely, initial screening, technical evaluation and selection of feasible AM processes, re-evaluation of the feasible process and production machine selection. Prior to the design phase, the methodology determines whether AM production is suitable based on the given design requirements. As the design progresses, a more accurate process selection in terms of technical and economic viability is performed using the analytic hierarchy process technique. Features that would cause potential manufacturability issues and increased production costs will be identified and modified. Finally, a production machine that is best suited for the finished product design is identified.

The methodology was found to be able to facilitate the design process by enabling designers to identify appropriate AM technique and production machine, which was demonstrated in the case study.

This study addresses the gap between the isolated product design and process selection stages by developing the decision-making methodology that can be integrated in product design stages.

The purpose of this paper is to show the impact of framing and groupthink both individually and simultaneously regarding the decision of career selection.

The samples of this study are determined by the purposive sampling method with accounting major students as the criteria. The number of observation is 178 accounting major students. Independent variables of the study are framing and groupthink, while the dependent variable is the career selection decision. Data used in this study are primary data that are obtained by questionnaire. The analysis techniques used are simple regression analysis, multiple linear regression and coefficient determination analysis.

The results of this study show that framing has a positive effect on the career selection decision and so does the groupthink.

Both framing and groupthink simultaneously have a positive effect on the career selection decision. The value of coefficient determination (R²) is 21.3 percent, and the rest of 78.7 percent is explained by non-researched variable.

The problem of evaluating potential suppliers has always been based on finding an optimal tradeoff between supplier’s performance consistently meeting firms’ needs and acceptable cost. The purpose of this paper is to propose a hybrid multi-criteria decision framework to quantify this qualitative judgment and reduce ambiguity in selection of suppliers in the era of Industry 4.0.

A hybrid intuitionistic fuzzy entropy weight-based multi-criteria decision model with TOPSIS is proposed. The authors make use of the intuitionistic fuzzy weighted approach operator for aggregating individual decision maker’s opinions regarding each alternative over every criterion. Additionally, the authors employ the concept of Shannon’s entropy to calculate the criteria weights.

Results obtained on the basis of the proposed hybrid methodology are analyzed against two more cases wherein the authors try to showcase the relevance of using IFS and entropy-based decision framework and find out the uniqueness of the proposed framework in supplier selection process.

The proposed model is apposite to solve management problem of supplier selection in two ways: aggregating individual decision maker’s opinion for each of the predefined criteria along with individual decision maker’s importance and ranking the suppliers based on both positive and negative ideal solutions using TOPSIS.

A robust framework incorporates not only suppliers’ performance but also provides weightage to key decision makers. Especially in the context of MCDMs wherein both qualitative and quantitative data is evaluated simultaneously, the proposed framework is unique in its practical implementation of reducing ambiguity in the supplier selection process.

Although most empirical studies find that competitive bidding can reduce the interest cost, the municipal bond primary market is dominated by negotiating offerings. The purpose of this paper is to investigate this dilemma by empirically testing two hypotheses: self-selection bias and decision inertia hypotheses.

Logistic regressions and Heckman procedures are used to examine data from the California municipal bond primary market.

The paper finds that while information asymmetry does affect the selection of underwriting approach, self-selection bias cannot explain the cost difference between the two sale approaches. On the other hand, decision inertia has the highest explanatory power in the selection of sale approaches.

This paper provides a new explanation for the “competitive sale dilemma” from the perspective of decision inertia. The authors document that state and local governments show a greater propensity of adhering to previous choices, particularly in a context in which the outcome is uncertain or actors have little knowledge in comparing the outcome of the alternatives.

The purpose of this paper is to present result obtained from a developed technology selection framework and provide a detailed insight into the risk calculations and their implications in manufacturing technology selection process.

The results illustrated in the paper are the outcome of an action research study that was conducted in an aerospace company.

The paper highlights the role of risk calculations in manufacturing technology selection process by elaborating the contribution of risk associated with manufacturing technology alternatives in the shape of opportunities and threats in different decision‐making environments.

The research quantifies the risk associated with different available manufacturing technology alternatives. This quantification of risk crystallises the process of technology selection decision making and supports an industrial manager in achieving objective and comprehensive decisions regarding selection of a manufacturing technology.

The paper explains the process of risk calculation in manufacturing technology selection by dividing the decision‐making environment into manufacturing and supply chain environment. The evaluation of a manufacturing technology considering supply chain opportunities and threats provides a broader perspective to the technology evaluation process. The inclusion of supply chain dimension in technology selection process facilitates an organisation to select a manufacturing technology not only according to its own requirements, but also according to the interest of its constituent supply chain.

The construction industry today is one of the biggest industries in the world. As projects continue to grow in complexity, project management continues to evolve. Contractor selection is a difficult task that owners and project managers face. Although previously researchers have worked on the subject of contractor selection, a comprehensive decision support system for contractor selection has not yet been developed. Recent reports of major delays and cost overruns in mega projects highlight the need for a model that is able to be flexible and comprehensive becomes evident. The paper aims to discuss these issues.

The research focuses on obtaining insights from field experts using both quantitative and qualitative methods. Then, a model was developed in the light of the data collected. Accordingly, the model was tested on a case study.

This paper presents a model for contractor selection that is wholesome in its take on the topic. The model incorporates both managerial and technical aspects of the problem. The model was tested on a case study and it was proven to be feasible in real world applications. The contractor selection decision support system serves the needs of both academics and industry managers, as an integral part of project management.

The model presented in this paper is innovative in its take on the problems. MCDA tools have been uniquely modified in this paper to cater to the needs of the selection problem while accounting for the criteria hierarchy that incorporates aspects that are instrumental for proper evaluation of a contractor’s likelihood of success.

Although the public sector has become a major employer of management consultants, no research has previously been undertaken to investigate the purchase of management consultants in the public sector context. Outlines an investigation into the purchase of management consultants by government departments, focusing specifically on the selection decision. The UK and Australia were examined to ensure that the findings were not merely local phenomena. Research focused on the public sector decision makers’ guiding procurement principle, value for money, and the criteria and information sources both used and desired to assist the purchase decision. It was found that there was widespread and relatively uniform understanding of the procurement principle, value for money, although there appeared to be a lack of connection between this principle and procurement practice. Public sector decision makers also believed that they had adequate although not satisfactory access to information upon which to base their decisions while, significantly, it was revealed that these decision makers did not believe the selection decision for management consultants was, overall, difficult. Concludes that the implications of this research are twofold. First, it highlights the issues of “corporate memory” and information management, and their impact upon informed decision making, and secondly it questions the applicability of private sector research to public sector practice.

The purpose of this paper is to introduce a quantitative method for assisting contractors to select appropriate projects for bidding by considering multiple attributes and integrating decision group member opinions.

The fuzzy technique for order preference by similarity to ideal solution (TOPSIS) method is used to help contractors make decision on project selection and the linguistic terms are defined for representing the triangular fuzzy numbers for ratings of alternatives and weights of criteria.

The selection of appropriate projects for bidding is a multiple attribute group decision‐making exercise. In a real decision process, there are many uncertainties and ambiguities, and time limitations mean that decision makers cannot always make precise judgments. The numerical example demonstrates that the fuzzy TOPSIS approach can be used to simulate the decision process in project selection, and the results provide contractors with valuable insight into the project selection problem.

Selecting appropriate projects for bidding is to use a contractor's limited resources more efficiently and increase the probability of winning contracts. Therefore, there is a need for a quantitative method to help contractors make better decision on project selection. That leads to the formulation of this paper. The fuzzy TOPSIS method can assist contractors to make better decisions in bidding.

To describe how decision-making in the selection processes of managerial successors in business families is influenced by the use of cutting-edge technologies such as AI.

Systematic literature review of 65 articles indexed in Scopus and in the main specialized journals on family businesses.

The integration of AI and algorithms, specifically in selection procedures, raises major questions and faces legal and ethical issues that affect employee performance, moral commitment and fairness in the processes. These aspects are important to ensure transparency, fairness and accountability as they provide insight into the practices of business families and how succession challenges such as the possibility of using signaling games and addressing gender biases and information asymmetries that have been reported in past research could be complemented by these actions.

The limitations of this research are mainly attributed to the exclusive use of a single database (Scopus), which could limit access to relevant literature; Furthermore, the exclusion of certain articles, despite focusing on prestigious journals on business families, may have overlooked relevant contributions; Furthermore, the 20-year scope of the literature review that ended in February and August 2024 omits subsequent publications that could have enriched the findings of this study.

To the best of the author’s knowledge, this study is the first of its kind to conduct a bibliometric analysis covering the line of successor selection and the process leveraged by new practices such as AI, an aspect that has been little addressed in the literature. In addition, this work traces aspects of decision-making that may affect selection. The research is of great value since it allows to illustrate in a consistent way the relationship between the selection of executive successors and how it is affected by the different decision-making processes in families, which allows to identify research gaps and make strategic decisions regarding the management of successions in BFs. Furthermore, this research provides a framework for future research in this area.

The purpose of this paper is to develop criteria for project manager selection based on desired skills of a project manager and facilitate the selection of a suitable candidate from a pool of potential candidates for the implementation of projects in the Indian context.

The study utilizes three major skills, namely human skill, conceptual and organizational skills; technical skill along with their sub-skills to develop criteria for project manager selection. Based on the responses of project professionals from industry, the study uses analytical hierarchy process to prioritize and identify the relative importance of different skills in the criteria in order to develop a hierarchical structure for project manager selection.

The study finds that at the first level of project manager selection criteria, conceptual and organizational skills are the most important selection criteria followed by human skills and technical skills. At the second level of project manager selection criteria, planning, delegating authority and understanding methods, processes, and procedures are some of the important sub-selection criteria. The weights indicating the relative importance of major selection criteria and sub-selection criteria can be used to evaluate the relative weight of a given candidate for selection as a project manager.

The results in this study are derived from specific demographic conditions in India. Future research with larger samples from other countries is needed for generalizations of the proposed criteria.

The proposed method quantifies the intangible qualitative criteria to select a project manager, which can aid decision-makers in a multi-criteria decision-making environment.

This research paper is focused on the identification of critical skills for the selection of a project manager, which is almost neglected by the researchers.

Supplier selection in the supply chain network (SCN) has strategic importance and involves multiple factors. The multi-criteria nature of the problem coupled with environmental uncertainty requires several procedures and considerations. The issue of decision-making in selecting the best among various qualified suppliers remains the major challenge in the pharmaceutical industry. This study investigated the multi-criteria multi-supplier decision-making process and proposed a model for supplier selection problems based on mixed-integer linear programming.

The concept of principal component analysis (PCA) was used to reduce data dimensionality, and the four best criteria have been considered and selected. The result is subjected to decision-makers’ (DMs’) reliability test using the concept of a triangular fuzzy number (TFN). The importance of each supplier to each measure is established using fuzzy technique for order preference by similarity to an ideal solution approach, and the suppliers have ranked accordingly.

This study proposes a mixed integer linear programming model for supplier selection in a pharmaceutical company. The effectiveness of the proposed model has been demonstrated using a numerical example. The solution shows the model's applicability in making a sound decision in pharmaceutical companies in the space of reality. The model proposed is simple. Readily commercial packages such as LINDO/LINGO and GAMS can solve the model.

This research contributed to the systematic manner of supplier selection considering DMs’ value judgement under a fuzzy environment and is limited to the case study area. However, interested researchers can apply the study in other related manufacturing industries. However, the criteria have to be revisited to suit that system and might require varying ratings based on the experts' opinions in that field.

This work suggests more insights practically by considering a realistic and precise investigation based on a real-life case study of pharmaceutical companies with six primary criteria and twenty-four sub-criteria. The study outcome will assist organizations and managers in conducting the best decision objectively by selecting the best suppliers with their various standards and terms among many available contenders in the manufacturing industry.

In this paper, the authors attempted to identify the most critical attributes to be preserved by the top managers (DMs) while selecting suppliers in pharmaceutical companies. The study proposed an MILP model for supplier selection in the pharmaceutical company using fuzzy TOPSIS.

Refractory materials are now used in all major industries that demand high-temperature resistance, including petrochemicals, steel, cement and aviation. Businesses must decrease operating costs, enhance product technology, sell well and manage corporate risks in decision-making, notably supplier selection, to be more competitive. The study aims to determine the key criteria and factors of supplier selection and to evaluate the importance of the key factor of the supplier selection criteria for the refractory materials manufacturers in Taiwan.

Analytical hierarchy process (AHP) is used to rank these factors for the decision maker. The AHP method is suitable for verifying refractory supplier selection criteria and providing references. The weighted loss scores for each supplier are then determined using the relative importance as the weights. Supplier selection criteria are ranked using their aggregate weighted loss scores. The provider with the lowest loss score should be chosen.

Product quality is the most significant of the five criteria: product quality, production technology, logistics capacity, service capability and supplier background. Professionalism is the most significant aspect of product quality, whereas equipment and capacity are vital in manufacturing techniques. The studies also show that the delivery rate is essential for logistics and service capabilities.

This research has important implications for refractory suppliers in promptly fine-tuning the production and service to enhance customer satisfaction, which is key to business sustainability.

The application of an AHP technique to a real-world industrial issue is what makes this research unique. This research addressed one of the most critical topics in supply chain operations by offering better judgement for supplier selection via the use of suitable quantitative methodologies.

This paper aims to propose a new approach called “analytical hierarchy process weighted fuzzy linear programming model (AHP‐FLP)” for supplier selection.

A hybrid method of supplier selection, AHP‐FLP is applied to a real industry case. The weights of the various criteria, taken as local weights from a given judgment matrix, are calculated using analytical hierarchy process (AHP) that are also considered as the weights of the fuzzy linear programming model. This new model is compared with the classical AHP method.

This study concluded that the AHP‐FLP method outperforms the AHP method for supplier selection with respect to restricted supplier selection criteria. Drawing on a real case, Supplier 1 was identified to be the best supplier through the AHP model under no restrictions, which contradicts the finding that Supplier 2 was selected as the best supplier by the AHP‐FLP model subject to constraints.

More research is definitely called for within the context of studying a more complex supply chain with multiple supply network and nodes. There is also a crucial need for investigating other hybrid methods to find the optimum supplier.

The findings of this study indicate that the weights of supplier selection criteria calculated by the AHP‐FLP model are in line with the actual supplier selection decision of purchasing managers. Since the AHP‐FLP model is relatively more difficult to implement compared with the crisp AHP, its application will be more appropriate for high‐value components where stringent purchasing criteria are required. In contrast, AHP remains an appropriate approach for relatively lower value components (C class).

The novelty of this study lies in the application of a hybrid approach to a real industry case. This study has dealt with one of the most important subjects in supply chain management, providing a better decision for supplier selection using appropriate quantitative techniques.

The purpose of this paper is to discover the criteria upon which selection decisions are made in six mobile libraries in New Zealand.

In this qualitative study data were gathered from interviews with six mobile librarians. The study is guided conceptually by the perceived need for a collection development policy and a selection procedures statement to guide selection, and the needs versus wants debate.

The data suggest that there is no universal approach to the selection of material for mobile library collections; mobile librarians achieve a balance between users' needs and wants to some extent when selecting; and there is no relationship between the nature of mobile library services and the serving of needs and wants. The researchers contend that mobile librarians' selection decisions are influenced by various criteria other than users' needs or users' wants.

This qualitative research is based on the analysis of the selection practices of six mobile librarians in New Zealand. Future research might seek to replicate this study's methodology using a larger population or investigate the lack of emphasis on reference services on some mobile libraries or the management of fixed, floating, and integrated mobile library collections.

Only a slight amount of literature exists about the selection of mobile library collections. This study will be of interest to mobile librarians in New Zealand and elsewhere who wish to obtain a better understanding of current selection practices. It will also act as guiding literature to help mobile librarians to make better informed selection decisions.

The study aims to explore the available academic literature on the decision-making frameworks used in additive manufacturing management (AMM).

This research formulates a systematic literature review to determine the research trend of the decision-making framework in AMM. Further, the theory, context, characteristics, and methodology (TCCM) framework is used to identify the research gaps and suggest future research directions.

The systematic literature review (SLR) delves into overarching research themes within decision-making frameworks in AMM. Additionally, it uncovers trends in article publication, geographical distribution, methodologies utilized, and industry applications. This review not only reveals research gaps but also proposes directions for future exploration.

The key novelty of this research lies in revealing the five most contributing themes of decision-making frameworks in AMM, with the highest contributing theme being AM process selection, followed by part selection for AM. This finding enables decision-makers to make informed decisions to address similar problems while exploring AM technology. Moreover, this research introduces an AM part fabrication roadmap inspired by the literature review. Lastly, the paper highlights key research gaps for future research.

The supplier selection problem has gained great attention in business management literature. The first objective of this study is to determine the required variables in selecting the best suppliers and to develop a supplier selection model based on these selected variables. The second objective is to explain how an integrated AHP‐PGP model can be used in supplier selection decisions while minimizing suppliers' defects rate, rate of late order delivery, purchasing costs, and maximizing suppliers' scores and after‐sales service levels.

An integration of an analytic hierarchy process (AHP) and multi‐objective pre‐emptive goal programming (PGP) is used to consider both quantitative and qualitative factors in selecting the best suppliers and allocating the optimum order quantities among them.

The integrated model is presented with a real‐world application using source data provided by the manufacturing firm operating in an automotive industry in Turkey. Findings demonstrate that the integrated AHP‐PGP model can be useful to all firms in their supplier selection decisions.

The integrated model presented in this study could be used to incorporate criteria such as quantity discount, demand satisfaction and budget constraints, etc. Further, the AHP approach could be extended with the genetic algorithm or data envelopment analysis techniques in solving such a problem.

Although there is considerable research in the literature, this study differs from the literature by introducing stages of the AHP model extensively, and adding a service objective function related to the suppliers' after‐sales service levels. In addition, the paper is especially of interest to both practitioners and researchers.

The paper addresses supplier selection in the public procurement sector. The motivation is providing a decision-making tool that mimics the intuitive behavior of the public tender committee in evaluating the bid according to predefined (quantitative and qualitative) criteria by a strict and transparent procedure, in accordance with governmental procurement regulations and requirements. To this aim, we address the supplier selection decision problem in the public procurement sector by a well-known fuzzy multiple criteria decision- making approach. Using the presented technique enables evaluation of both imprecise information and quantitative data in vendor selection, while keeping the transparency features requested in public procurement. We show the effectiveness and efficiency of the approach with a case study involving an Italian public administration.

Selection of logistics service provider (LSP) (also known as Third-party logistics (3PL) is a critical decision, because logistics affects top and bottom line as well. Companies consider logistics as a cost driver and at the time of LSP selection decision, many important decision criteria’s are left out. 3PL selection is multi-criteria decision-making process. The purpose of this paper is to develop an integrated approach, combining quality function deployment (QFD), and Taguchi loss function (TLF) to select optimal 3PL.

Multiple criteria are derived from the company requirements using house of quality. The 3PL service attributes are developed using QFD and the relative importance of the attributes are assessed. TLFs are used to measure performance of each 3PL on each decision variable. Composite weighted loss scores are used to rank 3PLs.

QFD is a better tool which connects attributes used in a decision problem to decision maker’s requirements. In total, 15 criteria were used and TLF provides performance on these criteria.

The proposed model provides a methodology to make informed decision related to 3PL selection. The proposed model may be converted into decision support system.

Proposed approach in this paper is a novel approach that connects the 3PL selection problem to practice in terms of identifying criteria’s and provides a single numerical value in terms of Taghui loss.

The purpose of this paper is to develop a decision‐making procedural hierarchy for evaluation as well as selection of third‐party reverse logistics provider (3PL) under fuzzy environment.

Due to uncertainty, vagueness arising from decision makers (DM) subjective judgment towards intangible (qualitative) selection criteria, fuzzy logic has been utilized to facilitate such a decision‐making process for 3PL evaluation and selection.

Evaluating and selecting 3PL providers can be regarded as a multi‐criteria decision making (MCDM) process in which a decision maker chooses, under several selection criteria, the best suited alternative. The present study highlights a case study on evaluation and selection of 3PL service providers for a reputed Indian automobile part manufacturing company. The fuzzy based decision‐making tool applied here has been proved fruitful for its effectiveness.

There are many research issues remaining in the development of this approach. First, the definition of appropriate fuzzy linguistic variables, corresponding membership functions (MFs) and their numbers, and their universe of discourse for a general use in the algorithm. Second, a methodology for accumulating raw data and analyzing the appropriate MFs for the base linguistic variables. Third, the relative importance of every decision maker, the decision‐making environment and structure may affect the decision‐making process. These have been assumed negligible in this study.

The main contributions of this research are: first, an integrated criteria list (followed by sets of sub‐criteria) has been modeled for service quality evaluation and appraisement of 3PL providers. Each sub criteria set has been structured to be preceded by a main criteria. Second, priority weights of various main criteria as well as sub‐criteria; extent of successful performance (rating) of different sub‐criteria have been expressed in fuzzy numbers. It facilitates in accumulating DMs subjective judgments into a unique numerical evaluation score. Third, decision makers risk‐bearing attitude has been estimated and utilized in computing overall evaluation index for alternative candidates. The decision‐making framework presented here can be extended to solve any decision‐making problem designed under a complex and interconnected set of primary criteria followed by sub‐criteria or more extended elaborate criteria hierarchy.

Supplier selection problem is the key process in decision making of supply chain management. An effective selection of vendors is heavily responsible for the success of any organization. Vendor selection problem (VSP) reflects a more practical view when the decision makers involved in the problem are present on different levels. Moreover, vendor selection consists of various random parameters to be dealt with in real life. The purpose of this paper is to present a decentralized bi-level VSP where demand and supply are normal random variables and objectives are fuzzy in nature. Decision makers are present at two levels and are called as leader and follower. As the next purpose, this paper extends and presents a solution approach for fuzzy bi-level multi-objective decision-making model with stochastic constraints. Different scenarios have been developed within a real-life case study based on different sets of controlling factors under the control of leader.

This study uses chance-constrained programming and fuzzy set theory to generate the results. Stochastic constraints are converted into deterministic constraints using chance-constrained programming. Decision variables in the bi-level VSP are partitioned between the two levels and considered as controlling factors. Membership functions based on fuzzy set theory are created for the goals and controlling factors and are used to obtain the overall satisfactory solutions. The model is tested on a real-life case study of a textile industry and different scenarios are constructed based on the choice of leader’s controlling factors.

Results showed that the approach is quite helpful as it generates efficient results producing a good level of satisfaction for the decision makers of both the levels. Results showed that on choosing the vendors that are associated with worst values in terms of associated costs, vendor ratings and quota flexibilities as controlling factors by the leaders, the level of satisfaction achieved is highest. The level of satisfaction of solution is lowest for the scenario when the leader chooses to control the decision variables associated with vendors that are profiled with minimum vendor ratings. Results also showed that higher availability of materials and budget with vendors proved helpful in obtaining quota allocations. Different scenarios generate different results along with different values of satisfaction degrees and objective values which shows the flexible feature of the approach based on leader’s choice of controlling factors. Numerical results showed that the leader’s control can be effectively incorporated maintaining satisfaction levels of the followers under various scenarios or conditions.

The paper makes a certain contribution toward the study of vendor selection existing in a hierarchical manner under uncertain environment. A wide set of data of different factors is needed which can be seen as a limitation when the available time is short for the supplier selection process.

VSP which is generally adopted by most of the large organizations is characterized with hierarchical decision making. Moreover, dealing with the real-life concern, the data available for some of the parameters are not complete, representing an uncertainty of parameters. This study is quite helpful for decentralized VSP under uncertain environment to reduce the costs, improve profit margins and to create long-term relationships with selected vendors. The proposed model also provides an avenue to explore the decision making when the leader has control over some of the decision variables.

Reviewing the literature available, this is the first attempt to present a multi-objective VSP where the decision makers are at hierarchical levels considering uncertain parameters such as demand and supply as per the best knowledge of authors. This research further provides an approach to construct scenarios or different cases based on the choice of leader’s choice of controlling factors.

Due to the current pandemic, the importance of logistics functions and decisions is well understood both at the level of companies and users. Logistics systems and related decisions are of vital importance in making supply chains effective, efficient and without disruption. Logistic pressure factors may emerge at different points along the logistics process, and given the role of logistics decisions as one of the important indicators of competitiveness, the determination of the logistics pressures that are likely to increase the costs of business, and their causative factors are a vital aspect of the logistics decision-making process. The study aims to provide assistance in the selection of the most ideal logistics decision by ranking the pressure factors affecting the logistics system, especially during the pandemic period for logistics enterprises operating in Ordu and Giresun provinces and which have a corporate identity.

In this study, it is aimed to make the most ideal logistics decision selection by ranking the pressure factors affecting the logistics system, especially during the pandemic period for the logistics enterprises operating in Ordu and Giresun provinces and having a corporate identity. For that purpose interval-valued Pythagorean fuzzy (IVPF)–analytic hierarchy process (AHP) based combinative distance-based assessment (CODAS) methodology was used. Additionally sensitivity and comparison analysis were discussed.

Competitive pressure was found as the most important pressure factor affecting the logistics system during the pandemic period. Change in regulatory rules was the pressure factor found to have the least effect on the logistics system. Using the weights of logistics pressure factors, “Operational Decisions” was found to be the most ideal logistics decision selection.

The findings provide support for the evaluation of logistical pressures and decision options by presenting a decision model capable of processing ambiguous information. During a pandemic or similar period, the study assists decision makers in determining a new route. The findings will also call business managers' attention to logistical pressure factors and lead them toward more realistic and feasible practices in the logistics decision-making process.

This study provided an effective and applicable solution to a decision-making problem in the logistics sector including logistics pressure factors and the selection of logistics decisions. In this context, a methodology was presented that will allow businesses to self-evaluate their own logistics pressure factors and the selection of optimal solutions.

The aims of this paper is to establish an appropriate physical-change-based renewal (PCBR) projects selection mechanism capable of selecting the combination of the PCBR projects that can make up an integrated urban renewal program in high-density cities.

The research design follows a sequential integrated methodology that combines the calculation algorithms of Fuzzy Analytic Network Process (Fuzzy-ANP) with Zero-One Goal Programming (ZOGP) to support decisions for the selection of PCBR projects. In the first phase, general criteria for assessing the sustainability performance of PCBR projects were collected from relevant literature. In the second phase, the Fuzzy-ANP was used to identify the priority weights of the candidate projects through clarifying the interdependent degree between the criteria and candidate projects. Finally, ZOGP method was selected as a predetermined number of PCBR projects among candidate projects.

The feasibility and effectiveness of this hybrid approach is then verified in a case study of Yuzhong District, Chongqing in China. The results of this study indicate that the integrated method is capable of directing the decision maker toward the best compromising solution of PCBR program that can achieve the maximization of sustainable benefits and allocate limited resources most efficiently.

The novelty of this paper consists in combining the algorithms of the Fuzzy-ANP method with those of the ZOGP model that serves as an effective analysis tool to address practical decision problems. This is the first hybrid algorithms to make PCBR projects selection decision that reach the maximization of the sustainable benefits, both in economic and socio-environmental terms.

The application of theories on group decision support is yet to be explored extensively in supplier selection literature, although the literature in both domains is extremely rich, in isolation. The purpose of this paper is to explore the application of group decision support theories for supplier selection.

The row geometric mean method (RGMM) of the analytic hierarchy process (AHP) has been used in this study for the prioritization of group preferences under consensus. A case study was conducted to test the theories of consensual group decision making and compare it with other approaches based on AHP.

The study establishes that the application of decision support theories for group decision making can improve the supplier selection process. Findings further imply that RGMM is more effective than eigen value method, for group decision making under consensus.

Methodologically, the study highlights the greater regularity in outcome of group decision making, vis-à-vis individual decision making, for the same decision-making context. Also, it highlights how RGMM is more effective since it preserves reciprocal properties and diversity in preferences better.

The study establishes that firms can improve supplier selection processes by leveraging on the collective expertise of a group rather than depending on individual decision-making expertise.

This study explores the application of different theories based on AHP for consensual group decision making. It compares different approaches based on AHP and establishes that RGMM is a superior approach for supplier selection.