Abstract

Currently, marketing is undergoing a major shift driven by environmental disruptions and advances in marketing technologies. This shift has implications for marketing decision-making. However, research on how marketing managers navigate modern marketing environments’ complex, volatile, and data-intensive nature is limited. This study addresses this gap by qualitatively analyzing marketing managers’ decision-making processes in 15 companies. Using the naturalistic decision-making approach and the situative perspective on cognition and action as theoretical lenses, we identify three key characteristics of decision-making in modern marketing environments—namely, agility, inventiveness, and reflexiveness. Our findings provide empirically grounded insights into the cognitive and behavioral processes involved in marketing decision-making and contribute to a deeper understanding of how managers navigate—and respond to—modern marketing environments’ challenges.

Introduction

In the current business landscape, which is characterized by continuous flux and uncertainty (Minciu et al., 2020), and simultaneously flooded with data (Bradlow et al., 2017), the nature of marketing is undergoing transformation. Marketing technology’s rapid development (Hoffman et al., 2022) brings increased opportunities to communicate and engage with customers, as well as follow and analyze their behaviors and sentiments (Holmlund, et al., 2020). These changing circumstances have altered marketing managers’ decision-making behaviors (Hoffman, et al., 2022). On the one hand, the availability of specific real-time data implies opportunities for marketing managers to make informed decisions to meet their customers’ dynamic needs and keep up with changes in the environment. On the other hand, the dynamics and complexity of marketing—combined with those of the business environment at large—challenge even experienced marketing managers (Moisander et al., 2020). Their current schemas for interpreting marketing problems and opportunities, including strategies for acting on these, might be insufficient or even obsolete (Laureiro‐Martínez & Brusoni, 2018). Accordingly, to provide valuable insights for both scholars and practitioners, we propose conducting further research on how marketing managers handle decision-making in the complex, data-intensive, and dynamic modern marketing environment.

Discussion of findings and contributions

This study aimed to investigate the nature of marketing managers’ decision-making behaviors and unravel the specific decision-making actions they engage in to handle today’s volatile, complex, and data-intensive marketing environments. Based on the empirical findings presented above, we suggest that a “modern” approach to decision-making in marketing can be described as the dynamic and purposeful optimizing of progress towards organizational goals in alignment with strategic agendas. By this we refer to the general nature of decision-making as an expedition in which marketing managers pursue the best possible outcomes in an ongoing exploratory act over time. This is as opposed to solving specific marketing problems or tasks as discrete decisions. We understand this approach to decision-making as a direct consequence of the volatility and complexity that signify today’s data-intensive decision-making scenes. The resulting uncertainties regarding appropriate courses of action and the potential consequences of different options call for a revised decision-making approach.

According to our study, DMM is characterized by agile, inventive, and reflexive behaviors realized through six groups of micro-level actions (surveilling, scanning, leveraging, reframing, corroborating, and challenging). The AIR model, shown in Fig. 1, integrates and visualizes our findings and conclusions. The visual metaphor of the triple helix illustrates the cyclical nature of the DMM and the dynamic interplay between micro-level actions and an evolving marketing environment. DMM is not agile, inventive, or reflexive. The DMM is simultaneously agile, inventive, and reflexive, and these characteristics constitute the three helices of the model. In sum, the agility of decision-making is a means of keeping up with the evolving outside world of customers and other stakeholders, which is not easily predictable. Inventiveness is about taking command of development, and proactively progressing the organization and its complex environment towards intended goals. Reflexivity holds everything together and ensures that decisions are as grounded and aligned with situational and contextual requirements as possible.

Abstract

Keywords

1. Introduction

2. Literature review

3. Research methodology

4. Case study

5. Results and discussions

6. Conclusions

7. Compliance with ethical standards

Declaration of Competing Interest

References

Abstract

Lean manufacturing is a profound system designed to enhance every manufacturing industry’s efficiency by reducing waste through internationally recognized tools and techniques. Manufacturing industries strive to adopt lean concepts to maximize their resources like staff, facilities, materials, and schedules to be economically effective. However, managers face difficulty selecting the appropriate lean tools out of the many available LM tools for successful lean implementation. This study suggests an innovative approach to choose suitable lean tools to maximize these essential resources. Herein the Value Stream Mapping and plant layout are considered for waste identification. Fuzzy QFD and FMEA prioritize the crucial resources concerning the defined wastes and determine the risk associated with each failure mode’s sub-element for lean application. It saves time by analyzing only the most critical resources for a successful lean implementation since its focus only on the most important resources. The applicability of the proposed approach is demonstrated through a case study of an Ethiopian shoe manufacturing firm. With the aid of future state plant layout and value stream map, total cycle time is reduced by 56.3%, lead-time is reduced by 69.7%, materials transportation distance and transportation activities are reduced by more than 75%, and workers required are reduced from 202 to 200.

1. Introduction

Companies adopt lean manufacturing (LM) principles and tools for various reasons, including global competition, an uncertain market environment, and rising customers’ expectations. The concepts of LM can make it possible to use their resources effectively and increase their competitiveness. According to Deif and ElMaraghy (2014) and Goshime, Kitaw, and Jilcha (2018), LM is characterized by doing more with less. It focuses on reducing/eliminating waste in order to increase productivity and maximize customer values (Belekoukias et al., 2014).

With the origin of the Toyota Production System, several LM techniques and tools were developed and used to achieve lean. Some of them are TPM, JIT, TQM, kaizen, kanban, production smoothing, cellular manufacturing, one-piece flow, Value Stream Mapping (VSM), and standardized work. Most of them are adopted in discrete manufacturers (Kumar & Parameshwaran, 2018). LM techniques and tools where effectively implemented in Automotive (Vamsi Krishna Jasti & Sharma, 2014), chemical (Jilcha & Kitaw, 2015), textile industry (Hodge, Goforth Ross, Joines, & Thoney, 2011), construction (Ko, 2010; Aka, Danladi Isah, Eze, & Timileyin, 2020), and healthcare industries (Barberato, Freitas, Godinho, & Francisco, 2016).

Abstract

     The rapid development of information and communication technology (ICT) has resulted in several improvements in diverse aspects of the organizational structures, including the introduction of virtual teams (VTs). Organizations rely on VTs since they bring a lot of benefits, such as the enhancement of organizational performance. However, effective VTs cannot exist without the proper implementation of decision-making processes. There is a lack of scientific research that attempts to understand the factors affecting decision-making processes in VTs. Studies in this area have only been conducted in the United States and Europe. However, such research has not been conducted in the Middle East, where specific scientific solutions are still required to improve the performance of VTs. Therefore, this study is conducted in the Middle East to gain scientific knowledge on this region’s specificity. Thus, the objective of this study is to identify the factors that affect VT decision-making processes. An online survey was used to collect data (Google forms) from companies in the IT industry in UAE, which are engaged in VTs. A literature review, survey methods, and structural equation modeling were used. The results showed that culture intelligence (CQ), transformational leadership (TL), and task conflict have a positive effect on VT decision-making processes, and relationship conflict has a negative impact on VT decision-making processes, which provides the management teams with a guideline on what to concentrate on in the measuring and enhancement of the effectiveness of VT decision making.

Introduction

     The most important changes that face organizations are caused by information and communication technology (ICT) (Al Majzoub and Davidaviˇciene˙ 2018). ICT has caused the formation of a new type of team work called virtual teams. The use of virtual teams is important for organizations because they allow team members to communicate, despite distance and time constraints. They allow organizations to hire the most talented people regardless of the location, which increases the efficiency of the team (Schmidtke and Cummings 2017). VTs are defined as “groups of people who are geographically and/or organizationally dispersed and who rely on collaboration technologies to carry out tasks” (Lowry et al. 2015). VTs bring advantages to organizations, such as the presence of diverse staff, innovation allocation of resources, flexible organizational structure, enhanced decision making and an increase in organizational performance (Bhat et al. 2017). Even though VTs provide benefits to the organization, they impose challenges to perform effectively. One of the essential factors that affects VT performance is decision-making processes (Wei et al. 2017). To date, there exists a case of scarcity in academic research in the field of decisionmaking processes in virtual teams (Davidaviciene et al. 2020; Drouin and Bourgault 2013; Tan 2019; Zakaria 2017). Most of the research is conducted on university students instead of actual organizational virtual teams (O’Neill et al. 2016; Organ and Flaherty 2018; Paul and Dennis 2018). From the literature review, it could be concluded that there is a lack of research that examines the effect of these three factors—conflict, culture and leadership—on the decision-making process in UAE.

Conclusions

     VT decision making is an important key indicator of VT performance, and hence organizational performance. Organizations must understand and measure the key factors that affect decision making and team performance in an organization and work to enhance it to be able to obtain its market share and be competitive. This article aimed to identify the following factors—CQ, TL, and task and relationship conflict—that affect VT decisionmaking processes. Based on the empirical research conducted, it can be concluded that CQ, TL, and task and relationship conflict are important factors to consider when measuring VT decisions, as the results show that culture intelligence (CQ), transformational leadership (TL), and task conflict are positively correlated with decision-making processes, and relationship conflict is negatively correlated with decision-making processes in VTs. This article provides a guideline for management in an organization to measure VT decision making. For example, when measuring decision making in VT, management should concentrate on how to enhance the following factors—cultural intelligence, transformational leadership, and task conflict—and how to eliminate or reduce relationship conflict factors between members in VTs. Measuring and controlling these factors will help an organization to enhance its VT decisions, which in turn will improve VT performance. In addition, this study proposes a framework for future studies to add factors to these models and create new models, not only to measure but also to implement strategies that can be used to enhance the VT decision-making process and hence improve VT performance. As all research does, this study has limitations. The first limitation of this study is the use of four factors affecting decision-making processes. However, there are other factors that affect decision making that were not included in this study, such as trust, ICT, etc. Second, the study was conducted among organizations in the IT industry. Future studies should include other industries. Third, this study was conducted in the U.A.E. Future studies should include countries in the Middle East in comparison with other regions. Future studies also should include all the mentioned limitations

Abstract

     The use of renewable energy technologies is a key factor for sustainable development but their selection from several alternatives is a difficult task that relies on the careful assessment of relevant criteria. While Multiple Criteria Decision Making (MCDM) methods have been used successfully in various renewable energy technology selection problems, the decision process becomes more challenging when preferential judgements are made on the basis of non-homogenous and imprecise input data, and when there is uncertainty due to disparities among decision makers. This paper presents a hybrid MCDM method capable of overcoming these problems by taking into account quantitative and qualitative data under a probabilistic environment in the context of group decision making. In this method, qualitative data is fuzzified and used along with quantitative data to develop a hybrid model. A coefficient factor allows decision makers to vary the weight of each quantitative model so that the resultant criteria weights and overall alternatives’ scores consider both subjective considerations and objective information. An example is presented to showcase the usability of the method developed for ranking and evaluating renewable energy technologies in the mining industry. In addition, the impact of different coefficient factors on the final results was assessed by means of sensitivity analysis. The results indicate that the method developed is able to minimise the loss of valuable objective information, caused by the subjective bias of qualitative weights during the evaluations, by adjusting the coefficient factors of the hybrid model during the calculations.

Introduction

     Energy-generating technologies that depend on non-renewable fossil fuels result in significant environmental challenges, such as increasing greenhouse gas (GHG) emissions, which lead to climate change (Disli et al., 2016, Li et al., 2020). In response to these challenges, it is important to better exploit renewable energy technologies (e.g. wind and solar), which are low-cost, clean and sustainable (Cunden et al., 2020, Dincer, 2000).

     The selection of renewable energy technologies is a complex and multidisciplinary problem that mainly refers to the performance of the technologies concerning multiple criteria such as environmental, social, technical and economic (Wu et al., 2018). In order to evaluate holistically and select the technologies that have a higher performance appropriately, decision makers need to have methodological tools that incorporate both quantitative and qualitative analyses of the multiple criteria. Decision makers should, therefore, make use of the best tools available to evaluate the performance criteria of renewable energy technologies. Choosing the best renewable energy technology to use among various alternatives considering conflicting criteria is thus considered a Multiple Criteria Decision Making (MCDM) problem (Büyüközkan & Güleryüz, 2017).

Conclusions

     A hybrid MCDM method was proposed and was applied to the selection of renewable energy technologies in the mining industry, which faces an increase in energy demand as high grade ores are depleted and the demand for metals and minerals, including those required for renewable energy technologies, increases. The large scale of mining operations makes it very important to consider renewable energy options in order to contribute to the sustainability of the operations.

     Three renewable technology alternatives, namely onshore wind, concentrated solar power, and solar photovoltaic, were assessed taking into account both subjective considerations and objective information with respect to five sustainability criteria. The selected criteria were potential total power generation, GHG emissions, area requirement, levelised energy cost, and prospective jobs creation. An objective weight was obtained using data compiled from the literature, whereas a subjective weight was obtained from the judgements and preferences of four experts. The proposed method was then employed to compute the criteria weights and the alternatives’ scores.

Abstract

1 Introduction

2 Literature review

3 Methodology used in research

4 Problem formulation

5 Proposed research methodology

6 Case study on ERP consultant selection problem

7 Sensitivity analysis

8 Discussion and conclusions

Declarations

References

Abstract

Enterprise Resource Planning (ERP) projects are risk prone due to software complexity, budget overruns, long duration, poor project delivery and a broad range of organization transformation processes. Companies are hiring experienced external consultants to achieve successful implementation of ERP system. Proper selection of ERP consultant is challenging task for any top management of organization because consultant fees accounts a major portion of project cost and this problem may be considered as Multi criteria decision-making problem. This article presents the hybrid approach of Fuzzy Analytical hierarchy process (F-AHP) and COPRAS-G in selection process of ERP consultants. F-AHP is used to obtain the weight of selection criteria and COPRAS-G is used to get the final ranking of consultants based on utility degree. The proposed model is illustrated with a numerical example whose data has been collected from survey in Indian industries. Finally, sensitivity analysis was performed to determine the robustness of the approached. The result of F- AHP highlights that reputation, ERP project experience and partner of ERP vendor and cost is an important selection criterion for selecting consultant and rank reversal problem has been greatly reduced by COPRAS-G. This proposed hybrid technique is quite flexible and provides efficient decision making in the selection of an ERP consultant.

1 Introduction
Companies are investing huge amount in information technology systems so as to meet the challenges of business organizations and smooth flow of information in the industries. ERP is an advanced information technology tool which supports business functions and information flow of an organization in a single software system. Shadi et al. (2019) has defined ERP is an Information Technology (IT) business solution which enables Organizations and their Stakeholders for management of project effectively & efficiently throughout the lifecycle of project. Thus, ERP system is amalgamation of business, technology and human resource. Implementation of ERP system has been considered among the most challenging investment projects due to the high costs involved, complexity and higher duration (Yusuf et al. 2004). Continuous high failure rates of ERP projects are major concern in recent years. Aloini et al. (2007)

1. Introduction

2. Methods

3. Results

4. Discussion

5. Conclusions

Acknowledgements

Annexes

References

Abstract

Motivation: Emergency Departments’ (ED) modern triage systems implemented worldwide are solely based upon medical knowledge and experience. This is a limitation of these systems, since there might be hidden patterns that can be explored in big volumes of clinical historical data. Intelligent techniques can be applied to these data to develop clinical decision support systems (CDSS) thereby providing the health professionals with objective criteria. Therefore, it is of foremost importance to identify what has been hampering the application of such systems for ED triage.

Objectives: The objective of this paper is to assess how intelligent CDSS for triage have been contributing to the improvement of quality of care in the ED as well as to identify the challenges they have been facing regarding implementation.

Methods: We applied a standard scoping review method with the manual search of 6 digital libraries, namely: ScienceDirect, IEEE Xplore, Google Scholar, Springer, MedlinePlus and Web of Knowledge. Search queries were created and customized for each digital library in order to acquire the information. The core search consisted of searching in the papers’ title, abstract and key words for the topics “triage”, “emergency department”/“emergency room” and concepts within the field of intelligent systems.

Results: From the review search, we found that logistic regression was the most frequently used technique for model design and the area under the receiver operating curve (AUC) the most frequently used performance measure. Beside triage priority, the most frequently used variables for modelling were patients’ age, gender, vital signs and chief complaints. The main contributions of the selected papers consisted in the improvement of a patient’s prioritization, prediction of need for critical care, hospital or Intensive Care Unit (ICU) admission, ED Length of Stay (LOS) and mortality from information available at the triage.

Conclusions: In the papers where CDSS were validated in the ED, the authors found that there was an improvement in the health professionals’ decision-making thereby leading to better clinical management and patients’ outcomes. However, we found that more than half of the studies lacked this implementation phase. We concluded that for these studies, it is necessary to validate the CDSS and to define key performance measures in order to demonstrate the extent to which incorporation of CDSS at triage can actually improve care.

Introduction

The growing demand for emergency services, combined with the priority sorting due to patient’s acuity, results in long waiting times for patients. Waiting times have a significant impact on patient mortality, morbidity with readmission in less than 30 days, number of preIntensive Care Units (ICU) resuscitation, length of stay (LOS), patient satisfaction and costs [1–7]. The outcome of patients’ medical treatment is time-sensitive, therefore the sooner the treatment is rendered, the better the outcome [3–7].

1- Introduction

2- Establishment of Internet of Things investment decision platform database

3- Investment decision model based on big data mining technology

4- Example analysis

5- Conclusion

References

Abstract

With the continuous improvement of the global securities market, the market competition is unprecedented fierce. In the aspect of investment decision support system, there is an urgent need to continuously absorb new information processing technologies and improve the scientific and standardization of decision-making, so as to achieve the goal of improving investment decision-making efficiency and stabilizing investment returns. Firstly, this paper introduces the defect that the database, model base and knowledge base are designed and implemented independently in the traditional decision support system. Then, constructing a unified and efficient data processing platform for Internet of Things based on DMFS technology, which realizes the integration of database and model, and innovatively establishes a data mining model facing market big data, dynamically analyzes and proposes investment decisions. Finally, the operating income data of a listed company in recent ten years are selected for simulation, which verifies the efficiency of the model system in processing dynamic data and the stability of investment income.

INTRODUCTION

With the continuous development of the global economy, the market situation is becoming more and more complex, and the investment decision-making of enterprises becomes more and more difficult [1]. As the most critical and important decision in all the decisions of an enterprise, mistakes in investment decisions will lead to huge losses for the enterprise. Investment decision usually refers to the investment decision made by the investor after investigation, analysis and demonstration of the enterprise or project [2]. Therefore, how to realize the accurate analysis of the investment prospect has become the research focus of most scholars[3]. He studied the investment decision-making optimization framework for energy-saving renovation of several buildings under the constraint of fiscal budget[4]. Proposing a multi-objective optimization model with economic objective as net present value and profit time and environmental objective as energy saving and emission reduction objective, and designing an intelligent optimization method combining particle swarm optimization and genetic algorithm, searching the investment strategy for transformation [5]. Gao constructed an analysis opinion model based on evidential reasoning rules, which taking stock reports of financial analysts as input and generates portfolio strategies through evidential portfolio[6]. Sevastianov and Dymova introduced Dempaser-Shafer theory and fuzzy set theory into an expert trading system that simulates human decision-making process, providing suggestions for traders to buy and sell stocks or other financial instruments by considering factors such as price history, technical analysis indicators, and recognized trading rules [7].

In this work we present a method for risk-informed decision-making in the physical asset management context whereby risk evaluation and cost-benefit analysis are considered in a common framework. The methodology uses quantitative risk measures to prioritize projects based on a combination of risk tolerance criteria, cost-benefit analysis and uncertainty reduction metrics. There is a need in the risk and asset management literature for a unified framework through which quantitative risk can be evaluated against tolerability criteria and trade-off decisions can be made between risk treatment options. The methodology uses quantitative risk measures for loss of life, loss of production and loss of property. A risk matrix is used to classify risk as intolerable, As Low As Reasonably Practicable (ALARP) or broadly tolerable. Risks in the intolerable and ALARP region require risk treatment, and risk treatment options are generated. Risk reduction benefit of the treatment options is quantified, and costbenefit analysis is performed using discounted cashflow analysis. The Analytic Hierarchy Process is used to derive weights for prioritization criteria based on decision-maker preferences. The weights, along with prioritization criteria for risk reduction, tolerance criteria and project cost, are used to prioritize projects using the Technique for Order Preference by Similarity to Ideal Solution. The usefulness of the methodology for improved decision-making is illustrated using a numerical example.

Introduction

In the last decade, the management of physical assets has emerged as a crucial business function for companies operating in asset intensive industries. Furthermore, the complex nature of modern engineered systems has led to the need for physical asset management as a discipline (Hastings 2015). Complex systems, composed of many interacting and interdependent components are increasing the likelihood of extreme, rare and disruptive events (Komljenovic, et al. 2016). As such, it comes as no surprise that the ISO 55000 Asset Management series of standards emphasize the need for risk-informed decisions (International Standards Organization 2014). Risk assessment in the asset management context requires the identification of what can go wrong (e.g. unexpected asset failures), characterization of the likelihood and consequence of such events, and comparison of the likelihood and consequence against risk tolerability criteria to determine risk treatment options (International Standards Organization 2018). Treatment options give rise to potential asset investments that must be prioritized while taking into consideration several factors (e.g. cost, return on investment, risk tolerability, etc.). In this work we present a framework and methodology for quantitative prioritization of risk-informed asset management projects using multi-criteria decision analysis. Several methodologies exist in the literature for multi-criteria decision making (MCDM), sometimes referred to as multicriteria decision aiding or multi-criteria decision analysis (MCDA). We use the acronym MCDM/A to consider both.

In this article, we introduce a new hybrid model called hesitant N-soft sets by a suitable combination of hesitancy with N-soft sets, a model that extends N-soft sets. Our novel concept is illustrated with real life examples. Moreover, we investigate some useful properties of hesitant N-soft sets and construct fundamental operations on them. We describe potential applications of hesitant N-soft sets in group decision-making, and finally we present some group decision-making methods as algorithms.

Introduction

Data related to most of our practical life problems including medical science, engineering, economics and environmental sciences among others, are imprecise and their corresponding solutions require the use of mathematical conventions based on imprecision and uncertainty. We cannot use traditional mathematical tools to overcome uncertainties existing in these problems. Consequently and in order to handle such uncertainties, a number of theories have been introduced including fuzzy set theory (Zadeh, 1965) and its extensions (Bustince et al., 2016, 2008), probability, rough set theory (Greco, Matarazzo & Slowinski, 2001, 2002; Liu, Qin & Mart´ınez, 2018; Pawlak, 1982), et cetera. Merig´o, Gil-Lafuente & Yager (2015) and Blanco-Mesa, Merig´o Gil-Lafuente (2017) are updated overviews of fuzzy research and fuzzy decision making with respective biblio-metric indicators. Anyhow all of these theories have their immanent difficulties (Paternain et al., 2012), a drawback that motivated Molodtsov (1999) to introduce the idea of soft sets as a new mathematical tool to tackle some of their difficulties. Soft set theory has significant use in game theory, smoothness of functions, medicine, operational research and probability theory (Alcantud & Santos-Garc´ıa, 2017; Molodtsov, 1999, 2004). Their algebraic analysis and applications developed rapidly. Maji, Biswas, & Roy (2003) presented some basic algebraic operations on soft sets and provide an analytical approach to theory of soft sets. Ali et al. (2009) suggested some different operations for soft sets and developed the idea of complement of soft set. They showed that certain De Morgan’s laws are valid in soft sets. Maji, Biswas, & Roy (2002) discussed the use of soft sets in decision making problems. It is observed that fuzzy sets, soft sets and rough sets are conveniently related notions. Maji, Biswas, & Roy (2001) combined soft sets with other mathematical structures and introduced an hybrid model called fuzzy soft sets, which is the natural fuzzy generalization of soft sets. They investigated many useful results related to this model. Optimization in this setting has been recently studied in Alcantud (2015, 2016a), Alcantud & Mathew (2017) and Liu, Qin & Pei (2017), see also Khameneh & Kili¸cman (2018) for an updated survey. Afterwards Majumdar & Samanta (2010) revised the definition of fuzzy soft set and proposed the concept of generalized fuzzy soft sets based on Maji, Biswas, & Roy (2003).

Given the importance of organizational whistleblowing, the purpose of this study is to investigate the relationships among individuals’ perceptions of moral intensity, ethical decision making, and whistleblowing intentions. Using information collected from employees’ working for varied firms, the analysis indicates that several dimensions of moral intensity, predominantly seriousness of consequences and social consensus, are positively related to components of ethical decision making, namely the recognition and perceived importance of an ethical issue, ethical judgment, and ethical intention. The results also show that the recognition and perceived importance of an ethical issue and ethical judgment are positively related to whistleblowing intention. Finally, seriousness of consequences is positively related to whistleblowing intention. The managerial implications of these findings are discussed, including the development of an ethical work context and programs that enhance ethical sensitivity. The research limitations and suggestions for future research are also highlighted.

Introduction

The frequency of whistleblowing has been increasing in organizations (e.g., Bhal & Dadhich, 2011; Bowen, Call, & Rajgopal, 2010; Clements & Shawver, 2011; Dasgupta & Kesharwani, 2010; Miceli & Near, 1985), often driven by corporate scandals that raise public concern over misconduct (e.g., Bhal & Dadhich, 2011; Dasgupta & Kesharwani, 2010; Mesmer-Magnus & Viswesvaran, 2005). Given their controversial role, whistleblowers are sometimes considered “problem employees” because they alert others to wrongdoing, thus harming the reputations of their employers (e.g., Barnett, Cochran, & Taylor, 1993). Alternatively, they can be recognized as legitimate contributors who are worthy of protection (e.g., Callahan & Dworkin, 2000; Lewis, 2011; Liu, Lio, & Wei, 2015; Near & Miceli, 1985) because their actions are beneficial and alert inside/outside parties of wrongdoing. This notion is especially pertinent in the post Enron-era, with whistleblowing being viewed favorably because of its role in preventing financial improprieties (see Gao & Brink, 2017). Legislation has also been passed to encourage and protect whistleblowing in the United States, including the Whistleblower Protection Act, Dodd-Frank Act, Sarbanes-Oxley Act, Fair Credit Reporting Act, and Consumer Protection Act (Bowen et al., 2010; Brink, Lowe, & Victoravich, 2017; Dasgupta & Kesharwani, 2010; Robinson, Robertson, & Curtis, 2012). Given this interest, it is important to identify the factors that prompt whistleblowing in the workplace – this study specifically focuses on characteristics elemental to the ethical decision-making process that likely motivate employees to report.

Identifying objectives is essential for decision making, but individuals have difficulties stating their important objectives. In public and environmental decisions, the diverse views of stakeholders should be included, but eliciting a broad set of objectives is challenging. We (1) study the effectiveness of individual brainstorming for eliciting objectives in a real-world setting; (2) test three interventions to support individuals in generating objectives; (3) investigate which and how many stakeholders are necessary to generate a comprehensive set of objectives; and (4) develop a feasible elicitation procedure for practice. In an experimental test, 71 stakeholders participated in five decisions about regional wastewater infrastructure planning in Switzerland. Three interventions were tested with an online survey procedure: (i) providing category cues, (ii) a perspective-taking task, and (iii) providing a predefined master list of objectives. In simple brainstorming, participants stated few objectives (M = 3.3) associated with 2.9 different categories on average. Participants consistently missed objectives they later considered important. Providing a master list was the only intervention that substantially increased the number and breadth of objectives (M = 12 objectives in M = 5.3 categories). With the help of our survey, participants generated between 30 and 38 distinct objectives for each decision case. Between five and nine participants were sufficient to generate these; more participants did not contribute new objectives. Most decision makers need help generating their objectives; combining simple brainstorming with a master list is a straightforward improvement that does not require a facilitator. An online process is promising for involving a large group of stakeholders.

Introduction

Knowledge about objectives is decisive for any analytical decision-making effort (e.g. Keeney, 1992). In a rational decision, the selection of an alternative is based on its achievement of objectives. Thus, in procedures such as multi-criteria decision analysis (MCDA), identifying objectives at the beginning is key (Gregory et al., 2012; Keeney, 1992; Reichert, Langhans, Lienert, & Schuwirth, 2015). As the objectives define the content of the evaluation, analysis, and discussion of alternatives, different sets of objectives can lead to different decisions (Brownlow & Watson, 1987). The omission of objectives, perhaps due to an oversimplified problem representation, can bias the analysis (Montibeller & von Winterfeldt, 2015; Payne, Bettman, & Schkade, 1999). Therefore, the set of objectives should be generated carefully. However, in actual decisions objectives are often not identified at all, or only cursorily (e.g. Gregory et al., 2012). For public and environmental decisions, the identification of objectives is both particularly relevant and particularly difficult. The normative demand is that these decisions serve the public interest. Objectives can be an appropriate level at which to operationalize this concept and formulate an integrated perspective on these decisions. Often, a few individuals take these decisions on behalf of a much larger group of stakeholders. However, how we can support these representatives in generating a set of objectives that addresses a wide range of concerns is an open question. It is also unclear how the public can be directly involved in this process. At first glance, the task of identifying objectives appears trivial: we just ask the decision makers to list what they want. However, the process of eliciting and structuring objectives is often considered “more of an art than a science” (Keeney, 1988). Bond, Carlson, and Keeney (2008, 2010) have found that individual decision makers are unable to fully articulate their objectives and without additional support neglect objectives they actually consider important.

A single reference point (SRP) is typically employed in traditional studies on the multi-attribute decision making (MADM) method. However, SRP lacks the advantage of the multiplicity of reference points and is thus unable to adequately describe loss aversion actions. With the goal of determining the loss aversion characteristics of decision makers, this work proposes an MADM method that is based on double reference points (DRPs) to solve the MADM problem using crisp and interval fuzzy numbers. First, this work describes the universality of decision-making problems with DRPs and then evaluates the characteristics of DRPs and their effects on decision making. Second, attitude and utility functions are established on the basis of the requirements of loss aversion. Third, considering the ‘‘one-vote veto’’ characteristic of a ‘‘dissatisfaction’’ attitude, a binary utility value is created by integrating the occurrence probability p, and the utility value u. The fourth, the main characteristics of and the aggregation method for the binary utility value are analyzed and established. Finally, a new decision-making method is applied to resolve the location decision of an agricultural product logistics center. Results indicate that the relationship between attribute and reference point significantly influences decision behavior. Compared with the traditional decision-making method, the proposed decision-making method can effectively identify the feasibility levels of alternatives. The ranking of the proposed decision-making method for feasible alternatives is basically the same as that of the traditional decision-making method. These results can effectively help solve agricultural engineering problems and broaden the coverage of and provide references for MADM research.

Introduction

Multi-attribute decision-making (MADM) problem refers to the ranking and selection of finite alternatives with multiple attributes [1], [2]. Owing to the MADM problems generally occurring in daily production and activities [1]–[5], the academic community has carried out comprehensive analyses. Today, studies on MADM problems come in two forms: those focused on perfect rational decision making and those focused on bounded rational decision making. The former is typically based on expected utility theory and is frequently adopted to solve MADM problems. The recent studies on the theory and method of MADM suggest that the research on MADM based on complete rationality has achieved substantial results [6], [7], [8]. The latter type is based on loss aversion theory. According to this theory, loss, relative to a reference point, produces greater psychological utility than the same amount of gain does [9], [10]. Loss aversion is a common phenomenon that is rooted in people’s decisionmaking actions and occurs in the areas of politics, economics, athletics, and so on [10]–[12]. Tom et al. even published a thesis on the science that proves loss aversion as a basic mechanism of human beings or animals [13].