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Identifying and prioritizing the risks are considered as critical issues in risk management; otherwise, non-considering the risks will lead to the problems such as delays in project implementation, increased costs, loss of reputation, loss of clients, reduced revenue and liquidity and even bankruptcy. The paper aims to discuss these issues.

In this paper, the factors influencing the organization risk tolerance level were identified. Then, the factors increasing and decreasing the risk tolerance level were determined by a decision-making model. Finally, a comprehensive model was considered for risk measuring and preparing a risk failure structure chart, in order to determine the factors influencing it as well as the measurement criteria and then they were ranked using the taxonomy method. In this study, the size of the statistical population was 130 (six small and medium manufacturer and service provider companies). Based on Cochran’s sample size formula, 97 questionnaires containing 30 questions were randomly distributed among the population. Validity and reliability of the questionnaire were confirmed. The data were analyzed by SPSS 22.

Given the hypotheses of this study, the first hypothesis was rejected and the other hypotheses were accepted. The final ranking was done using the taxonomy method; the personality of the project manager was ranked at first; income, credit and capital were ranked second and the number of personnel was ranked third. Moreover, the TOPSIS method was used for ranking to compare the results.

In this research, the identification and ranking of these factors have taken place in several small- and medium-sized organizations; in addition, the rankings are conducted using the taxonomy decision-making method.

In this study, research problem has been designed as a fleet-based optimization problem. This paper aims to present fleet modelling with risk taxonomy. Fleet modelling has been assumed as strategic multi-criteria decision-making problem to capacity building. Capacity building risk management is an essential element within the scope of its strategy to ensure sustainable corporate performance. Optimization is a fundamental target in aviation business’ strategy and management since the manager make decisions in their multi-interrelated criteria environment. Also, aviation is a highly regulated sector, and its operational and business procedures have certain limits by both national and international authorities. For this reason, companies implement risk management for strategic optimization while performing operations in compliance with the legislation. Risk management with capacity building and resource dependency perspective applied for strategic optimization aims to capture opportunities and result in threats with minimum accidents and incidents.

The taxonomy and analytical hierarchy process (AHP) have been identified as methodologies in this research. The type of training in the high organizational performance of an approved training organization, strategy, resources and allocations with the corporate objectives, the amount and qualifications of the flight crew, their professionalism, maintenance team and licenses, hangar conditions and capacity, authority requirements and limits, region conditions, altitude and meteorology, student profile, together with a multi-criteria decision are to be considered. For each criterion, there are resources and thus resource dependence. In this study, the analytical network process method was used. In the construction of new taxonomy, specific criteria have been considered, and the analysis has been accomplished as multi-criteria decision-making problem because of the relationship and interaction between them. A number of professionals with high knowledge of the pilots and manager from Air Traffic Organization participated in the study.

The fleet modelling is both strategic and operational decision issue for training organizations. In this issue, there is a vital problem as which aircrafts should include fleet? Main criteria and sub-criteria are analyzed by AHP method and sorted according to their priorities and the fleet qualifications consisting of the most suitable aircraft/aircraft are presented. The finding and suggestions will contribute to establish sustainable organization in based on capacity building and resource dependency for managers. While analyzing main criteria, the important criteria which were found were strategic and then operational. After ordering main criteria, sub-criteria were analyzed and were multiplicated with their items. According to study findings, aircraft suitability for training model is the most important item. It follows respectively aircraft maintenance sustainability, cost of aircraft supply and faculty budget adequacy. However, operation characteristics of the square that is less important item was found. It was seen that the strategies used to manage dependencies used the bridge strategy. The results we obtained with the interviews with pilot managers are very significant in terms of resource dependence on the subject of fleet optimization. While first criterion is operational, it continues with strategic and financial criteria. After interviews with pilot managers, it was figured out that maintenance is also very important criteria. For managing this dependency, university has acquisitions, which is one of the strategy to manage dependency, rather than outsourcing. For this reason, maintenance criterion has lower importance than others. When thinking of other criteria, strategic and financial criteria have played an important role. University has tried to decrease dependency and increase sustainability.

Aircraft selection is a strategic decision of fleet modelling in both aviation business and also training organizations via influencing their corporate performance, operational performance, capacity building and their sustainability. There are some factors that limit the criteria, as research problem has been developed for approved training organizations not airlines. For this reason, our research is limited with fleet of training organizations. Our findings and suggestions may be useful for flight schools to managing their resource dependency and also to their capacity building. In this research, new taxonomy has been developed depending on training organizations’ qualifications. Airlines may improve this taxonomy to use in their decision-making process.

The fleets, which were established considering the taxonomy in this study, will be able to manage the risk of resource dependency more successfully. Pilot candidates will be able to provide a more ergonomic and higher quality education. This research and its findings will contribute to the development of organizations’ accurate and timely decision-making skills. Resource dependency may threat organizational sustainability in our research, New taxonomy and our holistic approach will support organizational efforts to achieve sustainable strategies.

New taxonomy to modelling fleet that has been developed in this research may provide contribution to approved training organizations for both managing resource dependency-based risks and to capacity building-related decision-making process. This research may serve organizations as strategic decision-making tool. And also this kind of study may contribute to improve sustainability of organizations and serve more good fleet for their pilot candidates. For these reasons, this research may create social implications, as both resource using and capacity building will make contribution for society and add value.

This research presents new risk taxonomy and criteria. Also new taxonomy and its criteria are analysed with AHP. It is thought that this research shows risk management-based approach for fleet modelling creates benefits for approved training organizations to using their limited sources effectively and efficiently. The article includes risk management and capacity building-related approach to decision-making. also, this research presents modeling which will contribute to the management field besides literature. In developing taxonomy process, the analysis has been conducted, based on expert opinions and referred to for these pairwise comparisons. Airlines managers and risk managers may examine their fleet modelling according to our taxonomy which is based on risk management.

The purpose of this paper is to broaden understanding as to how certain social/personal dynamics influence credit decisions in Sri Lanka, elucidating them through a taxonomy and a conceptual typological matrix.

This research is an exploratory case study. The primary data collection methods are interviews and reconstruction of experiences. The data are complemented by documentary analysis and post-research events.

The research findings propose that credit officers and customers are influenced by six dynamics under three dimensions: the evaluation procedures (systematic/formal or heuristics); the relationship between customers and bank officers (personal or role relationship); and justification of credit (rational or irrational/situational). Based on the above results, a taxonomy of influential tactics and personality traits and a typological matrix are developed to classify credit decision-makers, who are labelled as BOSS, ROBOT, REBEL and BUDDY.

These case studies are from a private bank in Sri Lanka, hence it could affect the generalization of findings. Therefore, researchers are encouraged to investigate the plausibility of the findings in diverse cultural backgrounds.

Credit decision-makers and credit applicants could make use of these typological matrix and the taxonomy to understand each other and employ more influential approaches and appropriate influential techniques to make effective credit decisions. It also provides more insight into understanding the nature of credit-decisions and decision-makers and, provokes further research.

To the author’s best knowledge, this is the first study in Sri Lanka that considers certain influencing factors of credit decision-making and proposes a conceptual typology to understand those factors.

In the era of Big Data, larger volumes of data arrive in various forms at an increasing pace but of questionable quality and value. The abundant information (that emanates from these 5Vs – volume, variety, velocity, veracity, and value) taxes the bounded capacity of managers. This chapter introduces a taxonomy of approaches available for strategic decision making in an information-rich environment, several of which showcase that automation can help to augment (not supplant) managerial decision making. This taxonomy is then applied to an innovation context. Mapping a stylized version of the phases of the innovation process (i.e., front-end innovation, new product development, commercialization) onto the four decision-making approaches yields an organizing framework for understanding strategic decision making in the realm of innovation. The chapter concludes by identifying promising areas for future research.

This conceptual chapter: (1) explicates the foundational terminology regarding strategic decision making in a marketing context; (2) provides a primer on the era of Big Data and making strategic decisions in an information-rich environment; (3) introduces a taxonomy, which features approaches to decision making in an information-rich environment; and (4) applies the taxonomy in an innovation context to yield an organizing framework.

This chapter focuses on the nascent field that is emerging at the intersection of innovation, marketing strategy, and information-rich environments, and breaks new ground by exploring automation available to aid managerial decision making in this realm.

The main practical implication is to elucidate that managers can apply different approaches to decision making in today’s information-rich environment. Tables 2–4 provide to managers 12 examples of the types of decision making in an innovation context.

This chapter introduces a new taxonomy to classify four approaches for making strategic decisions in an information-rich environment, and extends that framework to the innovation realm. This framework aims to prompt researchers to explore important topics that exist at the intersection of innovation, marketing strategy, and managerial decision making in an information-rich environment.

Decision-making in human resources management is done at both the micro and macro level of organizations. Unfortunately, the decisions at each level are often executed without consideration of the other, and current theory reflects this issue. In response to a call for integration of micro- and macro-level processes by Huselid and Becker (2011), we review the extant literature on strategic human resources and high-performance work systems to provide recommendations for both research and practice. We aimed to contribute to the literature by proposing the incorporation of the situation awareness literature into the high-performance work systems framework to encourage the alignment of human resources efforts. In addition, we provide practical recommendations for integrating situation awareness and strategic decision-making. We discuss a process for the employment of situation awareness in organizations that might not only streamline human resources management but also result in more effective decisions. Additional considerations include implications for teams, boundary conditions (e.g., individual differences), and measurement.

This study aims to provide an integrated view of performance measurement systems (PMS) by developing a taxonomy reflecting the interdependencies among various PMS aspects. This study aims to move the study of PMS from a cartesian form of contingency fit to a configuration form. More specifically, the following research question is investigated in this study: To what extent do similar patterns across various dimensions of PMS occur with regularity? Using a survey approach to collect data from a sample of manufacturing firms, this study aims to develop a taxonomy based on three aspects of the PMS process, namely the design (i.e., the mix of financial, customer, internal processes, innovation and learning measures), the use (i.e., monitoring, strategic decision-making, attention-focusing, legitimization), and the revision (i.e., the addition, deletion, and changes in performance indicators). Three patterns of relationships reflecting the role and importance of PMS within the organization emerge: (a) PMS as an outcomes surveillance mechanism, (b) PMS as a management support tool, and (c) PMS as an institutionalized organizational process. This study contributes to the management accounting literature by providing a different understanding of the various levels of integration of PMS within organizational routines.

The purpose of this paper is to develop taxonomy of sourcing decision-making (SDM) archetypes and explore how different contextual factors influence these archetypes when global sourcing of complex components is considered a viable option.

A multiple case study approach with five in-depth cases is employed. In total, 19 interviews as well as publicly available and internal data from large buying firms headquartered in Austria and Germany were collected and analyzed.

The results reveal three different SDM archetypes which are described in detail (i.e. “consensus,” “argumentation” and “cabal”). Furthermore, it is found that these archetypes are mainly influenced by three contextual factors: sourcing maturity, product complexity and leadership style. The final model comprises six propositions which illustrate how these contextual factors determine companies’ SDM archetypes.

The study contributes to theory development at the intersection of organizational buying behavior and the (global) SDM literature. Thereby, it answers the call for more rigorous investigation of the influence of contextual factors on SDM processes.

The findings enable practitioners to better understand and consequently manage SDM processes and their outcomes. By supporting decision-makers in identifying SDM archetypes, this study allows sourcing managers and teams to make better decisions by avoiding problems that occur in situations in which the preferred decision-making type would result in suboptimal decisions.

The study provides a first step toward taxonomy of SDM archetypes and is among the first that explores their underlying contextual factors.

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 identify the technological risks in the context of open source software (OSS) and suggest an integrative OSS risk taxonomy.

The authors conducted an extensive literature review followed by expert interviews and applied the method for taxonomy development.

This research has identified an integrative OSS risk taxonomy composed of 8 categories with 51 risk items.

This taxonomy is a very useful tool for practitioners during the decision-making process when evaluating, assessing and calculating risks related to OSS adoption. Moreover, researchers can use it as a starting point for future studies to better understand the OSS phenomenon.

The purpose of this paper is to review and integrate the extensive literature base which examines judgment and decision‐making biases, to introduce this literature to the field of supply management, to create a valid, mutually exclusive, and exhaustive taxonomy of decision biases that can affect supply managers, and to provide guidance for future research and applications of this taxonomy.

The authors use a qualitative cluster analysis, combined with a Q‐sort methodology, to develop a taxonomy of decision biases.

A mutually exclusive, and exhaustive taxonomy of nine decision biases is developed through a qualitative cluster analysis. The Q‐sort methodology provides initial confirmation of the reliability and validity of the cluster analysis results. The findings, along with numerous examples provided in the text, suggest that supply management decisions are vulnerable to the described biases.

This paper provides a comprehensive review of the judgment and decision bias literature, and creates a logical and manageable taxonomy of biases which can impact supply management decision making. The introduction and organization of this vast extant literature base provides a contrasting perspective to much of the existing supply management research, which has incorporated the assumption of the rational agent, or what is known in the economics literature as homo economicus. In addition, the authors describe the use of qualitative cluster analysis and the Q‐sort methodology, techniques which have been used rarely if at all in within the field of supply chain management.