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Most decision-making problems in construction are complex and difficult to solve, as they involve multiple criteria and multiple decision makers in addition to subjective uncertainties, imprecisions and vagueness surrounding the decision-making process. In many instances, the decision-making process is based on linguistic terms rather than numerical values. Hence, structured fuzzy consensus-reaching processes and fuzzy aggregation methods are instrumental in multi-criteria group decision-making (MCGDM) problems for capturing the point of view of a group of experts. This chapter outlines different fuzzy consensus-reaching processes and fuzzy aggregation methods. It presents the background of the basic theory and formulation of these processes and methods, as well as numerical examples that illustrate their theory and formulation. Application areas of fuzzy consensus reaching and fuzzy aggregation in the construction domain are identified, and an overview of previously developed frameworks for fuzzy consensus reaching and fuzzy aggregation is provided. Finally, areas for future work are presented that highlight emerging trends and the imminent needs of fuzzy consensus reaching and fuzzy aggregation in the construction domain.

Fuzzy preference relations are considered and the concept of “fuzzy acyclity” is defined in order to measure their rationality. Such a concept is applied to study the properties of social welfare function in a fuzzy environment. In particular, the existence of non‐irrational aggregation rules is assured, in such a way that they can be used as a group decision aid.

SWOT (strengths, weaknesses, opportunities, threats) analysis is a powerful approach for evaluating the strengths and weaknesses of an organization with an internal perspective. The approach also takes into account the opportunities and the threats from an external point of view. These features make SWOT a commonly used approach in strategic management. The purpose of this paper is to propose an integrated SWOT analysis with multiple preference relations technique, to show the application of the proposed methodology, to prioritize the strategic factors and to present alternative strategies for ABC, a case company, which is targeting to use social media more effectively.

In this study, expert opinions are used to identify SWOT factors of ABC on social media. The obtained findings are evaluated and each factor is prioritized by means of the multiple preference relations technique.

The proposed evaluation model has four main groups, namely, strengths, weaknesses, opportunities, threats, under which 17 factors are identified. As a result of the evaluations, “O2: Opportunity to contact a large number of users simultaneously at affordable cost” has the highest importance level among other factors. Alternative strategies are developed based on the obtained results.

Decision-makers who have different backgrounds or ideas can state their preferences in different formats. Multiple preference relations technique is used to combine different assessments. SWOT analysis with multiple preference relations technique with a group decision-making perspective is proposed. This is the first time the method is used in the social media-related literature. With this study, the most appropriate social media strategic factors are selected for ABC and alternative strategies are determined based on the results.

The purpose of this research is to explore the critical success factors of mobile animation games, by exploring the game itself, information systems, game motivation and promotional activities, as well as conducting research and analysis on mobile animation game players.

This research used the Analysis Hierarchy Process (AHP) method and the consistent fuzzy preference relationship for data analysis. In this study, collect 1,286 valid questionnaires through online questionnaire surveys. And comparing the two games “Legend Showdown” and “Tower of Gods and Demons”, players believe that the more successful mobile animation game is “Legend Showdown”.

Through experimental design, and the consistent fuzzy preference relationship for data analysis. The results found that the critical factors considered by the player in relation to the mobile animation game were firstly the information system, followed by promotional activities, game motivation and finally the game itself.

Because this research does not involve the concept of fuzzy theory at all, it is easy to produce subjective, uncertain and ambiguity issues when comparing pairwise. We recommended that follow-up researchers can use fuzzy semantic preference relations to solve this problem.

This study proposed a new approach that takes the critical factors for the mobile animation game. According to the research results, the critical success factor of mobile animation games is the information system, as it could provide a reference direction for game manufacturers when designing or formulating marketing strategies in the future.

With numerous and ambiguous sets of information and often conflicting requirements, construction management is a complex process involving much uncertainty. Decision makers may be challenged with satisfying multiple criteria using vague information. Fuzzy multi-criteria decision-making (FMCDM) provides an innovative approach for addressing complex problems featuring diverse decision makers’ interests, conflicting objectives and numerous but uncertain bits of information. FMCDM has therefore been widely applied in construction management. With the increase in information complexity, extensions of fuzzy set (FS) theory have been generated and adopted to improve its capacity to address this complexity. Examples include hesitant FSs (HFSs), intuitionistic FSs (IFSs) and type-2 FSs (T2FSs). This chapter introduces commonly used FMCDM methods, examines their applications in construction management and discusses trends in future research and application. The chapter first introduces the MCDM process as well as FS theory and its three main extensions, namely, HFSs, IFSs and T2FSs. The chapter then explores the linkage between FS theory and its extensions and MCDM approaches. In total, 17 FMCDM methods are reviewed and two FMCDM methods (i.e. T2FS-TOPSIS and T2FS-PROMETHEE) are further improved based on the literature. These 19 FMCDM methods with their corresponding applications in construction management are discussed in a systematic manner. This review and development of FS theory and its extensions should help both researchers and practitioners better understand and handle information uncertainty in complex decision problems.

This study aims to identify significant factors, analyse them using the consistent fuzzy preference relations (CFPR) method and forecast the probability of successful deployment of the internet of things (IoT) and cloud computing (CC) in Gujarat, India’s building sector.

From the previous studies, 25 significant factors were identified, and a questionnaire survey with personal interviews obtained 120 responses from building experts in Gujarat, India. The questionnaire survey data’s validity, reliability and descriptive statistics were also assessed. Building experts’ opinions are inputted into the CFPR method, and priority weights and ratings for probable outcomes are obtained to forecast success and failure.

The findings demonstrate that the most important factors are affordable system and ease of use and battery life and size of sensors, whereas less important ones include poor collaboration between IoT and cloud developer community and building sector and suitable location. The forecasting values demonstrate that the factor suitable location has a high probability of success; however, factors such as loss of jobs and data governance have a high probability of failure. Based on the forecasted values, the probability of success (0.6420) is almost twice that of failure (0.3580). It shows that deploying IoT and CC in the building sector of Gujarat, India, is very much feasible.

Previous studies analysed IoT and CC factors using different multi-criteria decision-making (MCDM) methods to merely prioritise ranking in the building sector, but forecasting success/failure makes this study unique. This research is generally applicable, and its findings may be utilised for decision-making and deployment of IoT and CC in the building sector anywhere globally.

The companies are struggling to collect invoices due to the decrease in the economic growth. This global trend does not only affect undeveloped countries, but it also has a strong impact on the developed countries. Improving the debt collection process become a significant element to maintain financial stability. The institutions that are specialized on collecting payments, debt collection agencies and their call centers, with their expertise in the field can improve the payment process. Yet, managing evaluating the performance of debt collection agencies is a very hard process that involves uncertainty and imprecision. Performance measurement (PM) is a combination of numerically expressed characteristics which give insight about the success or degree of accomplishment of an activity. PM can be handled in various levels such as individual, team, department or company. The aim of this study is to present a systematic and objective PM method for call centers.

In this study, first an exploratory approach is used to understand the call center measurement problem. Several meetings are done with the representatives of both call center firms and the parent firms that outsource debt collection process. Simultaneously, a broad literature review is conducted. An iterative approach is selected to reach deeper knowledge on the process. New meetings are planned and scope of the literature review has changed based on this iterative approach. After these steps, the problem has been considered as the multi-criteria decision-making problem since more than one criteria should be considered for evaluating the performances of call centers. The result of the literature review and the meetings with experts show that defining the weights for the criteria is very crucial for evaluating the performances accurately. Collecting human judgment for defining the weights of call center criteria necessitates dealing with vagueness and uncertainty. The intuitionistic fuzzy sets excellent tools for representing uncertainty. Interval valued intuitionistic fuzzy sets can easily represent the human judgments. Thus, in this study, an intuitionistic fuzzy multi-criteria decision making approach is used to design the proposed methodology. Incomplete interval-valued intuitionistic preference relations are used to determine the weights of the indicators aggregating linguistic evaluations of the decision makers.

The proposed approach provides an objective calculation of performance measurement. In order to provide objectivity, indicator performance functions are proposed for the first time in this study. Nine different functions and related parameters are defined to objectively measure indicator performances.

The paper proposes an objective and easy-to-modify approach for call-center PM, which can be used by call center managers. It presents a new fuzzy multi-criteria decision-making (MCDM) method for call center performance evaluation, which can consider the multi-experts' judgments under vagueness and impreciseness, which may be conflicting and incomplete interval-valued intuitionistic fuzzy preference relations. Also nine new functions are defined for indicator performance.

Analyzing current recommender systems, it is observed that the cold start problem is still too far away to be satisfactorily solved. This paper aims to present a hybrid recommender system which uses a knowledge‐based recommendation model to provide good cold start recommendations.

Hybridizing a collaborative system and a knowledge‐based system, which uses incomplete preference relations means that the cold start problem is solved. The management of customers' preferences, necessities and perceptions implies uncertainty. To manage such an uncertainty, this information has been modeled by means of the fuzzy linguistic approach.

The use of linguistic information provides flexibility, usability and facilitates the management of uncertainty in the computation of recommendations, and the use of incomplete preference relations in knowledge‐based recommender systems improves the performance in those situations when collaborative models do not work properly.

Collaborative recommender systems have been successfully applied in many situations, but when the information is scarce such systems do not provide good recommendations.

A linguistic hybrid recommendation model to solve the cold start problem and provide good recommendations in any situation is presented and then applied to a recommender system for restaurants.

Current recommender systems have limitations in providing successful recommendations mainly related to information scarcity, such as the cold start. The use of incomplete preference relations can improve these limitations, providing successful results in such situations.

The purpose of this paper is to apply a fuzzy LinPreRa cognitive map to evaluate the interaction and importance of factors affecting the development of strategic alliance partnerships between the outlying island duty-free shops and existing collaboration firms in duty-free shops. Meanwhile, the key factors should be considered in establishing strategic alliance partnerships while analyzing and comparing the perspectives of owners for outlying island duty-free shop and partner vendors of collaborating firms along with differences of influencing key factors on partnership quality.

This study incorporates a fuzzy linguistic preference relation analytical network process (fuzzy LinPreRa ANP) in the fuzzy cognitive map (FCM) method to formulate a fuzzy LinPreRa cognitive map to evaluate the interactions and importance of key factors and the conditions of interactive impacts during the establishment of strategic alliance partnerships. The authors use the outlying island duty-free shops in Taiwan as the empirical subject to illustrate how the fuzzy LinPreRa cognitive map is applied. In-depth, interviews and questionnaire surveys are conducted to collect and evaluate respondents concerning key factors affecting strategic alliance partnerships establishment.

The following three findings based on the results of empirical analysis: first, the administrative behavioral patterns of managers for strategic alliance partnerships encompass shared values and goal coherence, while the associative statements are located on the first layer of fuzzy LinPreRa cognitive map core associations, which illustrates that businesses attach great importance to conceptual ideas. Second, integrity and reputations of both parties are the governing mechanism of strategic alliance partnerships, influencing mutual reputation. Third, the relationship of strategic alliance partnerships refers to the profit opportunities of both parties and their ability to respond to the market, including future development, regional indicators, marketing capabilities, brand multiplicity and customer retention. However, it can be inferred that such associative factors are located in the outer layer or belong to noncore associations, which means that both parties’ abilities to respond to market reactions are weakened.

This study provides valuable relationship managerial strategies to maintain long-term partnerships for outlying island duty-free shops and their alliance collaborating firms including strengthened relationships of both parties’ managers to achieve common values and consistent objectives; improved beneficial value of both parties in strategic alliance partnerships; continued close communications to enhance the quality of strategic alliance partnerships; and establishment of personnel training mechanisms and strict formulation of management rules for strategic alliance partnerships.

The main valuable contributions are included the fuzzy LinPreRa cognitive map by combining two different decision methods including FCM and fuzzy LinPreRa ANP is proposed to help decision makers to improve the evaluation quality and calculation efficiency for critical elements’ interaction and importance; the fuzzy LinPreRa cognitive map can clarify considering significant factors when maintaining strategic alliance partnerships and further provide valuable relationship managerial strategies to maintain long-term relationships for duty-free shop owners and their alliance collaborating firms.