Psychological Preferences Research Articles

Leveraging online reviews and expert opinions for electric vehicle type prioritization

The rapid expansion of the electric vehicle (EV) market in recent years has presented manufacturers with the challenge of strategically prioritizing the types of EVs in which to invest under uncertain conditions. This study proposes an enhanced multi-attribute decision-making (MADM) framework to address this issue by leveraging online reviews and expert opinions. The proposed framework combines a novel linguistic representation, called calibrated basic uncertain linguistic information (CBULI), to capture uncertainty, a value function from cumulative prospect theory (CPT) with double reference points to model the manufacturers’ psychological preferences, and the Preference Ranking Organization Method for Enrichment of Evaluations II (PROMETHEE II) for prioritizing EVs for investment. It extracts demand attributes from online reviews, applies CBULI to represent uncertain evaluations, and incorporates CPT to capture risk preferences. A case study of an EV manufacturing enterprise in Sichuan, China, was conducted to validate our framework, and the results demonstrated its effectiveness and practicability in identifying the most promising types of EVs for investment. The results of sensitivity and comparative analyses further confirmed the robustness and superiority of the model in comparison with prevalent methods. The work here contributes to methodological advancements in research on the choice of types of EVs in which to invest, and provides valuable insights for EV enterprises to make informed investment-related decisions that are aligned with user demands and enterprise development. The proposed MADM framework supports the strategic development of the EV industry by enabling manufacturers to prioritize investment in the appropriate types of EVs under uncertainty.

Literature Review: Why do we need innovative design methods for future Mobility-as-a-Service (MaaS)?

Since its inception in 2014, Mobility-as-a-Service (MaaS) has been the subject of numerous trials and pilot studies globally, predominantly yielding favourable outcomes. However, specific issues that need to be clarified regarding its long-term development have been observed. This paper outlines the development stages of MaaS and the trials conducted across different nations. It highlights an ambiguity within user-centred design in the extant literature, specifically the inadequate exploration of user requirements. The discourse propounds the necessity for a paradigm shift in the research focus—advocating for a more profound inquiry into user needs instead of the prevailing emphasis on the advancement of enabling technologies and the operations of digital platforms. Given the increasing demand for personalisation within intricate transportation systems, comprehending users’ cognitive and psychological preferences emerges as paramount. Such an understanding is imperative to allow the service framework to adapt and identify principal offerings that resonate with user inclinations. Moreover, establishing an enduring dialogue between service providers and users is essential. The paper argues for design researchers to formulate and implement an innovative methodology that fosters the alignment of supply with demand, thereby enhancing the efficacy of Mobility as a Service.

Assessment of Iranian airlines using network cross-efficiency DEA and the regret theory

Network Data Envelopment Analysis (NDEA) has been extensively applied to evaluate the air transportation sector. NDEA provides a tool for evaluating the internal processes of Decision-Making Units (DMUs). Optimistic Network Cross-Efficiency (ONCE) has recently been extended to the basic two-stage system. However, there are still two main shortcomings that need to be addressed. First, the ONCE evaluates DMUs based only on the optimistic viewpoint, neglecting the pessimistic viewpoint. The optimistic viewpoint assumes that there is only one set of reference points, which includes the best practice DMUs. The first contribution of this study is to develop a new Pessimistic Network Cross-Efficiency (PNCE) method. This method is based on a new set of reference points, which includes the worst-performing DMUs. The PNCE is developed as an extension of the ONCE. Second, both the ONCE and newly developed PNCE methods may lead to unrealistic results because they neglect the subjective preferences of Decision Makers (DMs). These NDEA models employ the Arithmetic Mean (AM) as the cross-evaluation aggregation method, which not only underestimates the importance of self-evaluation but also overestimates the importance of peer evaluations. Consequently, ONCE and PNCE may lead to biased efficiency results. To address this drawback, the second contribution of this study is to develop a new Aggregation method based on the Regret theory and Consensus (ARC). This method aims to reflect the psychological preferences of DMs when estimating cross-evaluation weights. To achieve this goal, we obtained new optimistic and pessimistic efficiencies by utilizing the newly developed ONCE-ARC and PNCE-ARC methods. Subsequently, a Double-Frontier Network Cross-Efficiency with ARC (DFNCE-ARC) is developed as a more comprehensive NDEA. Finally, a practical application is conducted to assess the performance of a set of Iranian airlines, demonstrating the usefulness and applicability of DFNCE-ARC.

Promoting Decision Making and Behavioral Change: Application of Psychology and Behavioral Economics to Nudge Design

Nudge design is a design approach that uses people’s psychological preferences and cognitive limitations to guide people to make choices. The theoretical basis of nudge design mainly comes from psychology and behavioral economics, among which limited rationality theory, cognitive behavior theory, and Fun theory are important theoretical frameworks. This paper summarizes the basic contents of these three theories and their application examples in nudge design. By analyzing the relevant cases of nudge design, the application effects of these three theories in different fields and scenarios are expounded, such as public transportation, environmental protection, health management, education and training, etc., to provide reference and enlightenment for the theory and practice of nudge design.

Effect of changes in surface visual properties of heat-treated wood on the psychological preference

Heat treatment of wood is an attractive, environmentally friendly modification, which can change surface visual properties of wood including color and grain, but it is unclear how heat-treated wood is perceived and evaluated compared with untreated wood. In this paper, Chinese fir was heat-treated at 160, 180, 200, or 220 °C for 2 or 4 h. The changes of wood surface color and grain contrast were measured. A subjective questionnaire and eye-tracking technology were used for psychological evaluation. The results showed that changes in the visual properties of heat-treated wood had a significant effect on psychological preference—heat-treated wood was generally more preferred than the untreated, particularly at 200 °C for 4 h. Grain contrast and hue played an important role in the preference for heat-treated wood. The preference gave people the positive psychological impression of warmth, weight, cost, prevalence, and comfort. Eye-tracking analysis showed that Chinese fir heat-treated at about 200 °C with high hue value and clear grain contrast was easier to gain more visual attention. The results would have a high technical reference value for the heat-treated wood in product visual design.

Research on the Influencing Factors of Online Tourism Product Purchase Intention in Ethnic Minority Areas—Taking Ganzi Tibetan Autonomous Prefecture as an Example

[Purpose] Fully explore the humanistic value of tourism resources in ethnic minority areas, explore the current situation and improvement direction of their tourism products, and promote the high-quality improvement of their tourism products. [Method] Taking online tourism products in Ganzi Prefecture, Sichuan Province as the research object, a covariance based structural equation model was used to study the impact of destination characteristics, tourists' perceived usefulness, perceived risk, and tourists' trust on the overall purchase intention of tourism products in Ganzi Prefecture from the perspective of consumers. [Results] Research shows that consumers' perceived usefulness, trust level, and tourism destination characteristics have a significant positive correlation with the overall purchase intention of tourism products in Ganzi Prefecture. Perceived usefulness is the core influencing factor that affects the overall purchase intention of tourism products in Ganzi Prefecture, and there is a negative correlation between perceived risk and the overall purchase intention of tourism products in Ganzi Prefecture. [Conclusion] In the design and creation of online tourism products in Ganzi Prefecture, it is necessary to fully respect and consider consumers' psychological preferences for tourism destination characteristics, perceived usefulness, perceived risk, and level of trust. The research results will provide management decision-making and reference for tourism product planning companies in ethnic minority areas.

Multi-stage probabilistic linguistic matching method with the screening mechanism and individual preference relationship fusion

Considering the diversification of transaction information and complexity of matching environment, the original information with the language tendency plays an important role. Thus, under this circumstance, this paper gives a matching decision-making system under the probabilistic linguistic environment so as to improve the matching efficiency and quality. First, this paper demonstrates the multi-stage two-sided matching problem and illustrates the system frame. Then, for the multiple indicators, a probabilistic linguistic integrated cloud Bayesian network is constructed to present the dependency relationship, and determine the corresponding probability. It is known that the accuracy of agents’ preference information acts on the stability of final matching results, which may involve the strong ranking, agents’ psychological preferences and personal interests, etc. Thus, the improved ORESTE (organísation, rangement et Synthèse de données relarionnelles, in French) method is introduced to derive strong ranking and determine preference, indifference, and incomparability relation (PIR). Furthermore, this paper constructs the dynamic two-sided matching model considering the screening effect. Finally, a case study in second-hand house transaction is used to demonstrate the matching process. Simulation and comparison analysis validate its feasibility and effectiveness.

Study on the time and scale of mutual aid for aging care under the background of active aging.

China has entered an aging society and will likely become the fastest-aging country in the world. The demand for aging care services has greatly increased. In recent years, the model of mutual aid for aging care has met the needs of older adults, especially those in rural areas. In this case, how much time should be spent on mutual aid for the older adult in terms of time and how much influence the size of the group has in terms of space are two very important questions when studying mutual aid for aging care. An overlapping generations model is built in this article, which includes representative agents, representative enterprises, the endowment insurance system constructed by the government, and the behavior of representative agents in mutual aid for aging care under the background of active aging. In the base case, the optimal proportion of time to participate in the mutual aid group is 9.31%, and the optimal proportion of time is influenced by the benchmark time of care and the size of the care group. With the increase in the benchmark time of care, the optimal proportion of time increases correspondingly, but the increase is decreasing. With the increase in the size of the care group, the optimal proportion of time decreases, but after the size reaches 4 or 5, the impact becomes very small. When parents' psychological preference coefficient changes from 0.1 to 0.9, that is, when parents change from introverted to extroverted personalities, the optimal proportion of time and parents' utility will also change. For children who usually take care of their parents for a long benchmark time, the optimal time to participate in the mutual aid group based on personal utility maximization is also long. Second, as the size of the group increases, the time for representative agents to participate in the mutual aid group gradually decreases. In addition, the model of mutual aid for aging care is highly correlated with parents' personalities, and extrovert parents benefit more from this model.

An Extended EDAS Approach Based on Cumulative Prospect Theory for Multiple Attributes Group Decision Making with Interval-Valued Intuitionistic Fuzzy Information

The interval-valued intuitionistic fuzzy sets (IVIFSs), based on the intuitionistic fuzzy sets (IFSs), combine the classical decision method and its research and application is attracting attention. After a comparative analysis, it becomes clear that multiple classical methods with IVIFSs’ information have been applied to many practical issues. In this paper, we extended the classical EDAS method based on the Cumulative Prospect Theory (CPT) considering the decision experts (DEs)’ psychological factors under IVIFSs. Taking the fuzzy and uncertain character of the IVIFSs and the psychological preference into consideration, an original EDAS method, based on the CPT under IVIFSs (IVIF-CPT-EDAS) method, is created for multiple-attribute group decision making (MAGDM) issues. Meanwhile, the information entropy method is used to evaluate the attribute weight. Finally, a numerical example for Green Technology Venture Capital (GTVC) project selection is given, some comparisons are used to illustrate the advantages of the IVIF-CPT-EDAS method and a sensitivity analysis is applied to prove the effectiveness and stability of this new method.

Analysing individuals' use of moped-sharing and their perception about future private car dependency

Moped-scooters are one of the latest services coming into the shared mobility ecosystem, which is why scientific contributions devoted to this actor are still scarce. This research explores two main questions. First, it analyses the determinants of the frequency of use of e-moped-sharing among adopters of these services. Second, it examines whether the use of e-moped-sharing may affect the individuals' perspective of the need to own a private vehicle in the future. To achieve this goal, we conducted a survey campaign in the four main cities of Spain (Madrid, Barcelona, Valencia and Seville) which gathered 3897 valid responses. This data was employed to build a Generalized Structural Equations Model that accounts for the influence of a variety of factors, including socio-economic characteristics, stated travel behaviour, and psychological preferences of the individuals. The results show that the frequency of use of electric shared-mopeds is higher among wealthier younger individuals and residents in the city centre. The modelling concludes that more frequent use of shared electric mopeds is negatively correlated with the belief that private vehicles will remain a necessity in the future. This effect seems to be more pronounced among high-income groups and those that combine e-moped-sharing with other shared services.

A hybrid generalized TODIM approach for sustainable 3PRLP selection in electronic manufacturing industry

Under the pressure of globalization, environmental protection/resource consumption, and the intensification of market competition, various industries have recognized the significant importance of sustainable supply chain management (SCM), in which reverse logistics (RL) has attracted many concentrations from governments to enterprises and the public. However, it is relatively difficult to operate RL autonomously for most electronic manufacturing companies whose core businesses and competition advantages are not in logistics operations because of limited resources and professional skills. Therefore, it is a good choice to escape from the predicament by selecting an appropriate third-party reverse logistics provider (3PRLP) with professional skills for outsourcing and accomplishing the RL business. Generally, evaluation and selection of a 3PRLP is regarded as a multi-attribute group decision-making (MAGDM) process, in which four aspects, namely, the evaluation attributes system, weight of experts and attributes, preference information representation and alternative ranking method have impacts on the decision-making results. Therefore, this paper constructs an MAGDM framework named generalized TODIM (TOmada de DecisãoInterativa e Multicritério, Portuguese acronym for Interactive multicriteria decision-making) under a hybrid information environment, which includes crisp numbers, interval numbers and fuzzy linguistic terms, for solving the problem. It provides a useful reference for manufacturing enterprises to find out the optimal 3PRLP from many alternatives. In particular, the use of different information forms for different evaluation attributes can not only facilitate decision makers to provide their judgments, but also effectively retain the original decision information as much as possible. Utilization of the generalized TODIM method is helpful for considering the risk attitudes and psychological preference behaviors of decision-makers. Finally, to demonstrate the feasibility and effectiveness of the proposed method, a case study relates to select a sustainable 3PRLP in an electronic manufacturing company was conducted. The findings show that the company should pay more attention to the attributes related to social responsibility, service quality and cost when selecting alternative 3PRLP under the sustainable SCM environment. It also provides useful suggestions for 3PRLP themselves to improve their competitiveness from these aspects. Moreover, the sensitivity and comparative analysis demonstrated the necessity and superiorities of comprehensively considering weights of experts and attributes, adopting hybrid information formats, and taking into account the risk attitudes and psychological preference behaviors of decision-makers during the evaluation and selection process.

Research on lean management and innovation capability evaluation of technological small and medium sized enterprises under probabilistic hesitant fuzzy sets

The lean management and innovation capability evaluation of technological small and medium sized enterprises is a classical multi-attributes group decision-making (MAGDM). Recently, the probabilistic hesitant fuzzy sets (PHFSs) have been extended to apply in many fields. However, the existing models don’t evaluate the alternative considering the psychological factors. Thus, in this paper, an extended probabilistic hesitant fuzzy grey relational analysis (PHF-GRA) method is proposed to reduce the restrictions of GRA method by combining with cumulative prospect theory (CPT), considering the psychological preference. In addition, the PHFSs assigns probability values to different degrees of hesitancy, which shows its superiority in complex environment. At the same time, the weight vectors of each attribute are calculated by the entropy values of different foreground decision elements. Then, probabilistic hesitant fuzzy GRA (PHF-GRA) model based on CPT model is constructed for MAGDM under PHFSs. Finally, a practical example study for lean management and innovation capability evaluation of technological small and medium sized enterprises is constructed to validate the proposed GRA (PHF-GRA) model based on model CPT and some comparative studies are constructed to verify the applicability.

Delving into Hearing Threshold of the Delay Gap of Initial Reflection in a Room by Using the Response of Cortical Brainwaves

In this study, the apparent variation ranges of acoustical parameters were investigated in a concert hall. The initial time delay gap (ITDG) was evaluated in terms of its just noticeable difference (JND) through two instruments, the cello and the trumpet. Even though the IDTG values were prolonged over the measurements and were not significantly varied, an ITDG range of 22–220 ms in increments of 91 steps was produced electro-acoustically in an anechoic chamber. The result of JNDc (Δgap/gap) was rated by 50% “different” judgement ranges for the cello and trumpet tracks, respectively. The effective duration of the autocorrection function (ACF) of the continuous brainwaves (CBWs) within the alpha (8–13 Hz) frequency range in the left hemisphere responding to 91-step ITDG increments revealed that the continuous ratios of τe_min ((τe_min_rear − τe_min_front)/τe_min_front) of CBWs were constantly on the trumpet. Furthermore, a homologous period of resonance between the subjective JNDc and τe values of the ACF of CBWs in the alpha range allowed us to conclude that the subjective JND of the ITDG in a room was related to the W_IACC value of the interaural cross-correlation function, which reflected the characteristics of source signals themselves and aroused the activities of the brain in the right hemisphere (p < 0.01). The dry sources of sound stimuli were first used to link the psychological preference and the neurophysiological activation of the room acoustics.

Learning utility of smart school learning space: the impact of spatial factors via visual stated preference method

ABSTRACT In the era of Education 4.0, the development of smart schools represents a significant step forward. Learning spaces are critical to the success of smart school construction in primary and secondary schools, due to their significant impact on school-age children. This study focuses on identifying key spatial factors that influence students’ psychological preferences and learning utility in smart learning spaces. To achieve this, a visual stated preference method was employed to design a questionnaire, which was used to survey primary and secondary school students in Harbin, China. The resulting data was analyzed using a discrete choice model, which enabled the measurement of the utility levels of different learning space factors. The results show that students’ learning space preferences are significantly influenced by a variety of spatial factors, with the combination mode of classrooms and outdoors having the highest level of influence, followed by a completely open interface, U-shape seating layout, zoned color tone, and the combination mode of classrooms and corridors; the students surveyed show a significant negative preference for heteromorphic spaces compared to rectangular spaces. The discussion recommends the design of smart learning spaces around key spatial factors to enhance the utility of student learning.

Multi-attribute decision-making based on comprehensive hesitant fuzzy entropy

It is unquestionable that hesitant fuzzy set has developed into an important tool for handling uncertain data. As a measure between data, hesitant fuzzy entropy is significant in the decision-making process. However, existing entropy measures have a series of problems such as anti intuition. In the decision-making problem under hesitant fuzzy frameworks, traditional decision-making methods such as TOPSIS method assume that the decision-maker is absolutely rational, which also makes the calculation results unreasonable. Therefore, the present study provides a comprehensive hesitant fuzzy entropy. Additionally, the traditional TOPSIS method is enhanced based on the cumulative prospect theory and the entropy method, thereby generating a multi-attribute decision-making model with unknown attribute weights. First, a new axiomatic definition of hesitant fuzzy entropy is given, then the comprehensive hesitant fuzzy entropy is defined based on the two features of fuzziness and unclarity. Besides, we prove that our method meets the new axiomatic definition. Finally, the improved TOPSIS method is applied to evaluate the comprehensive entropy measure, while simulations verify the effectiveness of the proposed method. The results showed that compared to existing entropy measures, the comprehensive hesitant fuzzy entropy more accurately reflects the uncertainty of hesitant fuzzy elements, and also has the advantages of simple calculation and easy understanding. Meanwhile, the improved TOPSIS method takes into account the psychological preferences of decision-makers, which is more reasonable than traditional methods.

Optimizing collaborative decision-making of multi-agent resources for large-scale projects: from a matching perspective

PurposeIn recent decades, infrastructure has continued to develop as an important basis for social development and people's lives. Resource management of these large-scale projects has been immensely concerned because dozens of construction enterprises (CEs) often work together. In this situation, resource collaboration among enterprises has become a key measure to ensure project implementation. Thus, this study aims to propose a systematic multi-agent resource collaborative decision-making optimization model for large projects from a matching perspective.Design/methodology/approachThe main contribution of this work was an advancement of the current research by: (1) generalizing the resource matching decision-making problem and quantifying the relationship between CEs. (2) Based on the matching domain, the resource input costs and benefits of each enterprise in the associated group were comprehensively analyzed to build the mathematical model, which also incorporated prospect theory to map more realistic decisions. (3) According to the influencing factors of resource decision-making, such as cost, benefit and attitude of decision-makers, determined the optimal resource input in different situations.FindingsNumerical experiments were used to verify the effectiveness of the multi-agent resource matching decision (MARMD) method in this study. The results indicated that this model could provide guidance for optimal decision-making for each participating enterprise in the resource association group under different situations. And the results showed the psychological preference of decision-makers has an important influence on decision performance.Research limitations/implicationsWhile the MARMD method has been proposed in this research, MARMD still has many limitations. A more detailed matching relationship between different resource types in CEs is still not fully analyzed, and relevant studies about more accurate parameters of decision-makers’ psychological preferences should be conducted in this area in the future.Practical implicationsCompared with traditional projects, large-scale engineering construction has the characteristics of huge resource consumption and more participants. While decision-makers can determine the matching relationship between related enterprises, this is ambiguous and the wider range will vary with more participants or complex environment. The MARMD method provided in this paper is an effective methodological tool with clearer decision-making positioning and stronger actual operability, which could provide references for large-scale project resource management.Social implicationsLarge-scale engineering is complex infrastructure projects that ensure national security, increase economic development, improve people's lives and promote social progress. During the implementation of large-scale projects, CEs realize value-added through resource exchange and integration. Studying the optimal collaborative decision of multi-agent resources from a matching perspective can realize the improvement of resource transformation efficiency and promote the development of large-scale engineering projects.Originality/valueThe current research on engineering resources decision-making lacks a matching relationship, which leads to unclear decision objectives, ambiguous decision processes and poor operability decision methods. To solve these issues, a novel approach was proposed to reveal the decision mechanism of multi-agent resource optimization in large-scale projects. This paper could bring inspiration to the research of large-scale project resource management.

Round offer prices in M&A transactions: costly negotiation and psychological preference

PurposeThe authors of this study aim to investigate possible explanations of the prevalence of price clustering in the final offer prices of mergers and acquisitions (M&A).Design/methodology/approachThe authors use final offer price in M&A deals to investigate the price clustering phenomena. The authors used regressions and logistic regressions to examine potential factors that might affect pricing strategy by looking into one-time acquirers and experienced serial acquirers.FindingsPrice clustering increases with negotiation uncertainties characterized as competitive bidding, number of bidders, challenged deals and duration. Moreover, the authors find persistent price clustering in experienced serial acquirers that are more experienced and better equipped with handling uncertainties, suggesting a preference of using round numbers regardless of levels of uncertainties. The authors' evidence shows that price clustering results from a combination of Harris' (1991) costly negotiation hypothesis where round prices may be used to lower search costs and psychological bias and preference.Originality/valueThe authors appear to be the first to investigate alternative theories that support M&A offer price clustering behavior, finding that both the costly negotiation and psychological bias and preference theories apply to M&A final price formation. Thus, the authors' major contribution, specific to the M&A process, is a clarification of physical and psychological factors associated with bidding and negotiation behavior. The authors are confident that the authors' study impacts conventional knowledge regarding M&A deal negotiation strategies, including bidding behavior, contract negotiation, financial analysis, management practices and risk management.

Wisdom of the Established Pattern

Wisdom of the Established Pattern

An integrated decision-making methodology for green supplier selection based on the improved IVIF-CPT-MABAC method

Green supply chain management attaches great importance to the coordinated development of social economy and ecological environment, and requires enterprises to consider environmental protection factors in product design, packaging, procurement, production, sales, logistics, waste and recycling. Suppliers are the “source” of the entire supply chain, and the choice of green suppliers is the basis of green supply chain management, and their quality will directly affect the environmental performance of enterprises. The green supplier selection is a classical multiple attribute group decision making (MAGDM) problems. Interval-valued intuitionistic fuzzy sets (IVIFSs) are the extension of intuitionistic fuzzy sets (IFSs), and are utilized to depict the complex and changeable circumstance. To better adapt to complex environment, the purpose of this paper is to construct a new method to solve the MAGDM problems for green supplier selection. Taking the fuzzy and uncertain character of the IVIFSs and the psychological preference into consideration, the original MABAC method based on the cumulative prospect theory (CPT) is extended into IVIFSs (IVIF-CPT-MABAC) method for MAGDM issues. Meanwhile, the method to evaluate the attribute weighting vector is calculated by CRITIC method. Finally, a numerical example for green supplier selection has been given and some comparisons is used to illustrate advantages of IVIF-CPT-MABAC method and some comparison analysis and sensitivity analysis are applied to prove this new method’s effectiveness and stability.

A Theoretical Examination of Homophily Beyond Focus Theory: Causes, Consequences, and New Directions

Scott Feld’s focus theory stimulated one of the most important traditions in the study of the concept of homophily in connection to individual action and network behavior across sociology and organizational studies. This article uses Feld’s focus theory as a starting point of reference to examine the major theoretical developments and empirical applications of homophily since his pioneering work. First, this article interrogates the causes of homophily by examining structured versus social psychological preference for similarity as two prominent explanatory mechanisms for homophily. Second, this article scopes out the consequences of homophily by examining the advantages and disadvantages of homophily for individual action and network behavior. Finally, building on the previous sections, this article proposes new areas for the study of homophily: deleterious psychosocial consequences of homophily, the role of multiplexity in causing homophily, and the disaggregation of homophily in general into different, nuanced (potentially interacting) types of homophily. This article thus contributes to the literature by offering a critical juncture to examine the key arguments that have guided the study of homophily since Feld’s focus theory and an important launching point for future research on the concept of homophily and its applications.