This bibliometric analysis investigates the evolution and current state of Corporate Social Responsibility (CSR) research within family enterprises. Despite the growing body of literature, gaps remain in understanding how contextual differences shape CSR in family firms, particularly in underrepresented regions such as Asia and Africa. Moreover, limited attention has been given to emerging factors like technological innovation and their implications for CSR implementation. To address these gaps, this study systematically analyzes 275 articles published between 1990 and 2024 in the Scopus database, aiming to answer three key research questions: (1) How has the scholarly discourse on CSR in family businesses evolved over time? (2) What are the dominant theoretical frameworks and methodological approaches in this field? (3) What are the primary collaboration networks and intellectual structures shaping this research domain? For the first research question, the analysis shows that publications on CSR in family businesses have increased substantially over time, with influential works such as Dyer and Whetten (2006) and Campopiano and De Massis (2015) laying the foundation of the field. Much of the early discourse centered on socioemotional wealth (SEW), while more recent studies have expanded to themes of sustainability, legitimacy, and regional contexts. However, contributions from underrepresented regions such as Asia and Africa remain limited. With regard to the second research question, the findings indicate that SEW and stakeholder theory are the dominant theoretical frameworks, reflecting the balance between financial and non-financial goals in family firms. Quantitative methods remain the primary research approach, often supported by bibliometric and secondary data analyses. For the third research question, the analysis highlights well-established collaboration networks concentrated in Europe and North America, particularly among scholars such as De Massis, Campopiano, and García-Sánchez. These networks have contributed significantly to the intellectual structure of the field, but cross-regional collaboration remains weak. Limited contributions from developing economies underline the need to broaden participation to capture more diverse perspectives. These findings offer useful perspectives for scholars and practitioners by identifying research gaps and future directions, particularly regarding technology innovation's impact on CSR and varied socio-cultural contexts of family enterprises.
This paper contributes to advancing qualitative and mixed-methods research in business by offering a comprehensive framework that rigorously validates qualitative findings through an explanatory sequential mixed-methods approach. Qualitative research broadly investigates and exposes facts from an epistemological perspective. However, challenges related to the generalisation and validity of the qualitative results require further inquiry, specifically in computer science and business technology research, such as business data analytics. Thus, this article aims to guide researchers in sequentially validating qualitative research methods using Grounded Theory Methodology GTM, bias reduction, and Structural equation modelling (SEM). The study suggests three sequential stages of mixed-methods research design — the research methods employed (GTM) to extract qualitative categories. Data was collected from postgraduate students using semi-structured interviews that were analysed using a rigorous (GTM) coding technique. Accordingly, results underwent a process of bias reduction as an initial phase of validation of qualitative results, and finally, results were validated using quantitative SEM analysis. The proposed framework meticulously demonstrates how an ordered set of stages in a research design can validate qualitative results while steering a study in conceptualising Business Analytics (BA) as an academic field. The research makes significant contributions to the practical and theoretical implications of qualitative results, mixed methods research design, and validation processes in business computing education research fields, thereby enhancing the understanding and application of these methods in real-world scenarios.
Research in sustainability accounting and reporting has expanded significantly, driven by the increasing demand for sustainable business practices. Yet, while quantitative approaches have significantly advanced sustainability accounting research, they may not fully capture the complex and multifaceted nature of sustainability issues. Integrating qualitative perspectives can complement these approaches by providing deeper contextual insights and enriching the overall understanding of sustainability phenomena. This paper argues that combining quantitative and qualitative methods through mixed methods offers a strong alternative to improve research outcomes. Mixed methods allow researchers to blend numerical data with contextual narratives, offering deeper insights into the motivations, challenges, and impacts behind sustainability accounting practices. The paper describes the current dominance of quantitative approaches in sustainability accounting research. It also highlights the underutilization of mixed methods and explains how integrating both approaches can address the weaknesses of single-method designs. The discussion briefly introduces different types of mixed methods designs to help guide future research. It also outlines challenges in applying mixed methods, such as higher resource demands and the difficulty of integrating different types of data. A synthesis of recent sustainability accounting literature reveals that although interest in mixed methods has increased, full methodological integration remains rare. To address this gap, the paper emphasizes the need for methodological flexibility and the strategic use of triangulation to enhance research rigor. By presenting updated examples and offering practical recommendations, this study contributes to advancing the methodological landscape of sustainability research. Incorporating mixed methods not only addresses existing research gaps but also enables a more comprehensive understanding of corporate behaviors, stakeholder relationships, and broader societal impacts. Future research should explore innovative designs that combine experimental and qualitative inquiries to strengthen the field further.
Purpose: This paper demonstrates the analyses of the role of educational and family support for the development of personal skills and risk willingness, which are important for pursuing entrepreneurial ventures. Entrepreneurship is seen as a critical component of economic development and growth, particularly in developing countries. Design/Methodology: The data was collected from three universities ((SIMAD University, Somali National University and Mogadishu University) in Banadir region purposively selected based on their research publication, formation of entrepreneurial sites, entrepreneurial fields, and entrepreneurial faculties. The study randomly selected 306 students across eight faculties from total population of 350 using Slovin’s Formula, the study utilized multiple linear regressions as technique of data analysis. The study used structural models through PLS software. Findings: The results revealed that that educational and family support significantly and positively impacted entrepreneurial intentions. The role of mediating variables (personal skills and risk willingness) also improves entrepreneurial intentions among graduate students. Moreover, the model shows that the changes of independent variables explain 34% of the changes of dependent variable, while the remaining 62% of changes might explained by unidentified factors which are not incorporated in the model. Recommendations: Thus, it is recommended that the private financial institutions and public institutions work together to establish micro finance center for the position of inspiring entrepreneurship joint ventures or ventures in the country.
Despite its essentiality, selecting the most appropriate research design is consistently challenging for many emerging researchers including postgraduate students, attributable to the lack of a formal approach. The process becomes more cumbersome and challenging when selecting more than one research design in a study. Consequently, many aspiring researchers sometimes select designs that do not align with the objectives of their studies. This study aims to propose a model that guides selecting the most appropriate research design for a study. The qualitative approach was employed, and it involved interviews with emerging researchers and postgraduates at a large public university of about thirty-five thousand students in South Africa. The findings reveal a three-step approach, based on which the criteria-based model (CBM) was developed. The study highlights the attributes of the CBM, which can be used to advance research methodology. The proposed CBM has significant implications for improving the selection of appropriate research design for a study. The implications include knowledgeability and alignment, from both theoretical and practical perspectives.
This study presents a methodological framework for measuring digital trade development in ASEAN, employing a mixed-method research design to evaluate five critical measures: health systems, human security, economic integration, digital transformation, and sustainable future. The research methodology integrates quantitative analysis of data from 30 digital trade exporters across six ASEAN countries with qualitative thematic analysis following Boyatzis' approach, incorporating document analysis, semi-structured interviews, and focus group discussions. A dedicated literature review highlights gaps in existing frameworks and informs methodological choices, while statistical power analysis validates adequacy for detecting significant differences despite modest sample size limitations (n=30). The analysis reveals statistically significant disparities in implementation levels across ASEAN countries (ANOVA: F(5,24)=12.34, p<.001), with digital transformation emerging as the most significantly implemented measure (M=3.70), followed by human security (M=3.63), economic integration (M=3.57), health systems (M=3.53), and sustainable future (M=3.37). Post-hoc analysis (Tukey's HSD) indicates significant differences between advanced tier and emerging tier countries (p<.001) and between intermediate and emerging tier countries (p<.01). Cross-case analysis identifies three distinct implementation tiers: advanced implementation (Singapore), intermediate implementation (Indonesia and Malaysia), and emerging implementation (Vietnam, Philippines, Thailand). These findings provide actionable insights into digital trade development strategies tailored to tier-specific challenges and opportunities. The findings demonstrate the effectiveness of mixed-method approaches in business research, particularly in examining complex regional economic phenomena. This research framework contributes to the existing scholarship on digital trade development in regional economic communities through the use of rigorous methodological approaches and comprehensive analysis. The findings have both theoretical and practical implications for researchers and policymakers studying the evolution of digital trade, as well as for filling critical gaps in existing measurement frameworks for developing regions. The study contributes to academic discourse by enhancing measurement frameworks for developing regions while informing policy decisions aimed at fostering inclusive and sustainable economic growth in ASEAN. By combining rigorous statistical analysis with qualitative insights, this framework offers a method to quantify and compare the level of digital trade development across different regional economic communities, contributing to the broader understanding of their trade evolution and informing future policy decisions.
This study investigates the mediating role of knowledge sharing in the relationship between business intelligence and strategic ambidexterity within Jordanian telecommunication companies. Utilizing a descriptive analytical approach, data were collected through an electronic questionnaire distributed to 350 managers, yielding 269 valid responses analysed via Structural Equation Modelling (SEM) with Smart PLS 4.1. The methodological rigor of employing SEM allows for a nuanced examination of the complex interplay among latent variables, which include business intelligence (with constructs such as Data Mining, Data Warehousing, OLAP, and Reporting) and strategic ambidexterity (focusing on Exploration and Exploitation).Findings reveal that all latent variables exhibit significant importance, with business intelligence positively impacting strategic ambidexterity, mediated by knowledge sharing. These results advocate enhanced knowledge sharing practices within organizations, enabling internal experts to leverage insights into external opportunities through well-structured business intelligence reports. Overall, this research contributes to the advancement of methodology in business and management by establishing a robust framework for analysing the mediating effects of knowledge sharing, while providing actionable insights for enhancing strategic decision-making in the telecommunications sector. Future studies may further explore the dynamics of these relationships across different industries, thereby enriching the field of business management research.
In the banking sector, managing liquidity risk is paramount to ensure financial stability and resilience. This study is motivated by a quest to determine the appropriate research methodology that satisfies both theoretical and practical aspects of designing and developing a system that integrates qualitative factors, specifically news sentiment, into liquidity risk forecasting for risk managers to rely on and use the predicted results. Previous works reveal a significant theoretical gap in liquidity risk prediction, highlighting the necessity for a methodology that bridges theoretical advancements and practical applications. The primary questions focus on evaluating how well Design Science Research (DSR) handles short-term liquidity risk prediction and the influence of qualitative factors on these predictions. The DSR approach in this study involved iterative phases of problem identification, artifact creation, and rigorous evaluation. A predictive model was developed, intertwining news sentiment analysis with quantitative liquidity ratios derived from Basel III principles. The results demonstrate that the model achieves an 86% accuracy rate in theoretical evaluations and an impressive 95.5% in real-world scenarios, outperforming traditional methods. This integration of qualitative factors into the predictive model enhances accuracy, providing a more comprehensive understanding of liquidity risk dynamics. By meeting its objectives, this study answers the posed questions that DSR can be used as a research methodology that validates not only the theoretical aspect of the problem but also the practical application of the framework. The study contributes to advancing risk management practices and suggests future work directions, reinforcing the importance of DSR methodology and similar methods considering qualitative dimensions in banking liquidity risk assessment. This advancement paves the way for more proactive and informed decision-making processes in banking institutions.
This article is a study introducing a new qualitative research methodology - Intuitive field research or IFRes - involving words and the narrative and relying on the experience and intuition of the [experienced practitioner] researcher (Stein, 2019). Though similar, it is different to autoethnography as the latter’s focus is seen to be on culture (ethnography) whilst IFRes may focus on any aspect – including, also, machine-type interactions. IFRes is a six-step process, described herein, which seeks to take advantage of considerable previous work experience, in the field, to answer a research question posed following a literature review. It is an iterative process which seeks to perfect the knowledge produced (Baldacchino, Ucbasaran & Cabantous, 2023). Intuitive Field Research (IFRes) emerges as a pioneering qualitative research methodology that capitalizes on the nuanced intuition and rich field experiences of researchers to uncover deep insights into complex phenomena (Stein, 2019). Distinct from autoethnography, IFRes introduces a structured six-step process designed to systematically harness and refine these insights for academic and practical application. Originating at the University of Aveiro, this method represents a significant departure from conventional research methodologies by valuing experiential knowledge and intuitive understanding as critical components of the research process. In the context of business and management, IFRes holds particular promise for addressing the intricate challenges of contemporary business environments. These environments demand an agile and nuanced understanding that transcends traditional quantitative analyses, making the case for methodologies that can capture the subtleties of consumer behavior, organizational culture, and innovation dynamics. By enabling researchers and practitioners to integrate their intuitive judgments with rigorous academic inquiry, IFRes offers a unique approach to exploring and solving pressing business and academic issues. This article delineates the foundation of IFRes, its methodological underpinnings, and its potential applications within business and management, illustrating how intuitive insights can drive innovation, strategic decision-making, and transformative organizational practices. Through this expanded lens, IFRes not only contributes to academic discourse but also provides practical frameworks for businesses seeking to navigate the complexities of modern markets and organizational challenges. A practical example of applying Intuitive Field Research (IFRes) in business and management could involve a multinational corporation seeking to enhance its customer experience across diverse markets. By employing IFRes, the corporation's research team could immerse themselves in different cultural contexts, using their intuition and experience to gather nuanced insights into consumer behavior and preferences (Gorry & Westbrook, 2013). This approach would allow them to identify subtle, culturally specific factors influencing customer satisfaction that traditional surveys or data analysis might miss. These insights could then inform tailored strategies for each market, leading to improved customer engagement and loyalty. This example illustrates how IFRes' emphasis on intuitive understanding, combined with rigorous analysis, can address complex challenges in global business environments, leading to innovative solutions and competitive advantages. This article on Intuitive Field Research (IFRes) significantly impacts research by offering a novel method that blends intuitive insights with rigorous academic inquiry. It addresses the need for methodologies that go beyond traditional quantitative analysis to capture the complexities of human behavior and organizational dynamics (Ganzarain, Ruiz & Igartua, 2019). By emphasizing experiential knowledge and intuitive judgment, IFRes empowers researchers and practitioners to uncover deeper understandings of complex issues. This approach fosters innovation, enhances strategic decision-making, and facilitates transformative practices in various fields, thereby enriching academic discourse and offering practical solutions for real-world challenges.
A growing body of academic research addresses issues related to questionable choices and errors in the use of research methods in published business research. These problematic research method practices (PRMPs) may be purposeful or unconscious, but they reduce the rigor of academic research and can harm the accumulation of scientific knowledge. Yet, absent from much of this literature is a theoretically grounded approach to understanding why these problematic practices occur. Prior scholars have summarized specific types of PRMPs, but attributions about their causes are primarily limited to research lack of motivation or poor doctoral education. While these may certainly be at play, the current manuscript proposes that the deeper psychological phenomenon of cognitive bias is a likely explanation. Cognitive biases occur when human cognition produces an outcome that is systematically distorted from objective reality (Haselton, Nettle, and Murray, 2016). More colloquially, cognitive biases are systematic errors that humans make when they are faced with perceiving, remembering, and understanding information. These unintentional biases are particularly likely when that information is voluminous and ambiguous. Cognitive biases are explained by two theories—heuristic theory and fuzzy trace theory. Heuristic theory suggests that humans default to using mental shortcuts as a means to make decisions more efficiently (Chaiken and Ledgerwood, 2012). Further, fuzzy trace theory explains how memory and reasoning can be flawed (Reyna and Brainerd, 1995). Because of the limitations of the human mind, heuristic theory and fuzzy trace theory act to create unintentional cognitive biases. The current manuscript argues that the cognitive biases of source confusion, gist memory, repetition effects, bandwagon effects, and confirmation bias are mostly subconscious means by which researchers make errors in research methods use. We argue that these biases are not a useful part of the didactic approach to research, but are rather mental shortcuts that can limit researcher effectiveness. Next, specific PRMPs are addressed: reliance on methodological myths and urban legends, errors in citations, use of questionable research practices, and inappropriate use of artificial intelligence (AI) tools and technology in research. Finally, there are a number of insights and recommendations derived from research on cognitive biases to assist scholars in promoting research methods best practices. In particular, researchers can combat cognitive biases by recognizing what they are and by providing more transparency about research methods use in their articles. Incentives for authors and reviewers may reduce the impact of cognitive biases on PRMPs. Editors should create and share clear guidelines on the use of AI in research. In summary, this manuscript addresses those critical issues, fills a gap in current research regarding why PRMPs occur, and provides researchers with key insights to effectively combat cognitive biases.
To date, there has been no proposed method to statistically validate Venn diagrams. We seek to correct this shortcoming. This paper is a review of a proposed method that offers the possibility of statistically validating Venn diagrams through the lens of the management vs. leadership debate in business. Through this research, we demonstrate a way to statistically validate Venn diagrams by using a modified method of exploratory factor analysis (EFA). First, when performing EFA to validate a Venn, we suggest the scree plot of eigenvalues will indicate how many circles should be in the diagram. Additionally, when normally conducting EFA, cross-loaded items are removed. However, when using EFA to validate a Venn, we propose items that cross load should be retained and placed in the corresponding intersection of the two (or more) circles of the diagram. Applying this method to a sample of 431 (n=431) employees aged 25 years or older, we created a statistically validated Venn diagram that identifies those skills that are uniquely management, uniquely leadership, and the overlap as reported by employees. As a result, this research provides scholars with the opportunity to classify actions as leadership or management based on their placement within the statistically validated Venn diagram of management skills and leadership skills. Importantly, through the application of this new research method, we bring the possibility of statistical confirmation to many of our social science theories that are represented by Venn diagrams. In the Discussion section, we offer a critique of possible limitations of the method and mistakes that researchers can make when applying this method.
The paper cogitates on the critical advent of 4th IR focusing on the concept of machine learning (ML) underpinned by natural language processing (NLP) to demonstrate how research philosophies and paradigms can be better taught and learned for students' benefit. A systematic literature review was earmarked for its depth and textual inquiry from scholarly arguments. The main purpose of this paper is to aver a progressive technological approach towards better comprehensive research paradigms and philosophies, which are complex domains with diverse variety in higher education and a cause of discomfort for students at post-graduate levels. Using quantitative algorithm, the natural language processing and machine learning-inspired digital model poses questions that place students in a reflexive mode and draws their articulated responses as inputs that model their worldviews against a host of philosophies in the database. The paper revealed that, discordant with previous scholars who advocated for a single philosophical assumption for a field, subject or researcher, such as the existence of a pure positivist and/ or pure interpretivist, purist philosophical assumptions should be challenged to benefit students and academics. It means that the digital discovery of research philosophies and paradigms extends the work of previous theorists to the technologically inspired discovery of episteme, ontology and axiology. By its nature, the use of NLP becomes an advanced channel on how we know what we know and the nature of the reality and values being displayed. The paper contributes to the evocation of deep learning arising from new philosophies and methods. The inquiry-based teaching approach transforms learning from the generic push-teaching method that assumes universality to the fostering of a reflexive approach that helps resolve the deep ideological approaches that caused the polarisation. The manner in which NLP and ML are able to extract information relevant to knowledge or philosophical discovery paves the way for approaches that can lead to the depolarisation and decolonisation of research philosophies, which can ultimately boost the development of research students.
The instruments that are constituted by inquiries that intend to investigate the opinions, behaviors and attitudes, instead of putting the person to the test, intend to find out how they would act in a given situation. Although there are no right or wrong answers, there is a tendency to respond in a socially acceptable way, even if the answer does not correspond to reality. This problem can be overcome through the Q-sort methodology that combines quantitative and qualitative data and analytical techniques that are not present in other methods. In this way, it consists of presenting the participants with a set of statements on a given topic and asking them to classify them according to their opinion, according to a predefined distribution, which is generally approximately normal. This methodology forces participants to distribute the score among the items on the scale, thus avoiding the constraints associated with social desirability and the tendency to respond in the same way or always through the midpoint to different questions. Another advantage is that it provides linearity and near-normality to the scale, which makes it possible to compare subjects more easily. Nevertheless, its advantages, Q-sort methodology also has negative points because forced-choice measures produce ipsative data that lead to distorted scales and problematic psychometric properties. As the data are obtained by ordering a set of items or by forcefully choosing one item over another, it is impossible to achieve very high or very low values on all scales, which gives rise to a large number of negative values that, in turn, result in an average correlation between the scales, which is also negative. In view of the above, it was considered relevant to apply the Q-sort methodology to a personality inventory, whose data were collected from 175 university students attending the Portuguese higher education institution which specializes in the area of economic and business sciences. The Q-sort methodology plays a crucial role in personality inventories by offering a subjective and personalized approach to assessing personality traits. It enables a more thorough and contextual analysis of individual traits, thereby contributing to a deeper and more comprehensive understanding of the human personality. The results of the empirical study showed that despite the mean values being negative or very close to zero, they allowed the grouping of respondents according to their similarities in terms of their personality traits depending on the course they attend.
The maturity model (MM) and Delphi research areas are extensive and diverse, leading to numerous approaches. This study addresses the Delphi method regarding its rigor requirements within the IS literature. To this end, the example of maturity model development is investigated. Hence, Delphi studies for MM development are identified and analyzed regarding their rigorous application and design. The examination focuses on the connections between maturity model aspects and the Delphi methodology. Hence, relevant aspects of maturity model and Delphi literature are elaborated, and criteria for methodological rigor are derived. A key challenge is linking the method to the specific design objective (in this study example, the development of a maturity model). After conducting a literature search to identify studies that use the Delphi method for MM development, these criteria are used as a basis for deductive content analysis. The results indicate a lack of clarity regarding the methodology, as different aspects are reported, although the general demands, starting points, and goals were similar. A need for design guidelines for planning and conducting Delphi studies is emphasized and addressed in this paper. Hence, guidelines for designing Delphi studies are developed and presented, considering relevant aspects for ensuring rigorous implementation. The focus lies on the linkage between study design and intended model elements, as this demonstrates the complexity of the study design and the relevance of the design decisions through an example. The guidelines integrate the different methodological aspects of maturity model development and the Delphi methodology, providing an orientation framework for the design process of such research projects. Therefore, this study contributes to existing research by proposing design guidelines for Delphi studies to foster rigor in the specific context of maturity model development. Although the presented guidelines focus on the maturity model context, the general design approach and decisions are transferable and applicable to other domains. Hence, this research contributes to the Delphi literature by providing insights into how relevant elements should be addressed in the designing process of a Delphi Study. Scholars should investigate how the presented guidelines must be adapted for other domains in future research.
In recent years, companies operating in activities such as dentistry, optometry, physiotherapy, or veterinary have seen the demand for their services grow. Their customers do not require their services only for health reasons but also for aesthetics and welfare issues. As a result, these companies compete in expanding and profitable markets. However, this business context has been detected by many professional entrepreneurs who decided to run their activity in healthcare and set up a firm without the necessary assets and knowledge. To overcome these liabilities, some seek partners who provide them with the resources they do not have by entering into different alliances. In contrast, others choose to compete under an independent business model. This paper sheds light on the factors influencing the decisions about the implemented business model in small knowledge-intensive firms by examining the association between the perception of the institutional environment variables and the dotation of intangible resources. For that purpose, a qualitative comparative analysis (QCA) was performed. Due to government regulations in force in these sectors, and methodological reasons, the study sample consisted of 88 small Spanish firms (less than 15 employees). The data were collected by a questionnaire distributed in 2017. We find that the choice to remain self-governing or to enter into a partnership (e.g franchising) is heterogeneously motivated by the evaluations that entrepreneurs have about the role of institutions concerning their activities and how high they consider their intellectual capital compared to their main competitors. In terms of institutional capital, these entrepreneurs refuse to implement patient loyalty policies and strive to have high-quality human capital in terms of the training & experience of their professionals. Moreover, the results also showed that independent business models pay little attention to market influences and view a certain level of regulation favorably, suggesting vocational and conservative behavior
This is a wide-ranging paper that discusses a number of issues surrounding the nature and use of data in academic research. As this is a vast subject the authors consider it a short note on this most important topic. It is a noteworthy fact that very little attention has been given to reflection on and understanding of the nature of research data. It seems to have been taken for granted that researchers would intuitively know what data is and how it should be handled. And interestingly this has not historically been an issue but in the light of the proliferation of multi-forms of data it is appropriate to reconsider the nature of research data and discuss how it is used in academic research processes. This is no trivial matter as many of the issues involved can often be used in imperfectly defined ways and thus there is a continuous propensity to ambiguity. The realisation that data is primarily a catalyst to human thought processes is an important insight to what data is really about. The main outcome of this paper provides a fresh or freshly invigorated insight leading to a novel conceptual understanding of the nature, role and potentiality of research data and this leads to emphasising the central importance of the researcher understanding what data will facilitate his/her answering the research question. The issue of the importance of data management is also emphasised as are the challenges of data interpretation. As an aid to future researchers, the paper offers a visual depiction of “The roadmap from phenomenon to idea to pursue”. The discussion in this paper is primarily philosophical although it does venture to address some of the more operational issues related to the effective use of data. The findings benefit from, and are underpinned by, the authors’ experiences over many years of practical empirical research. The authors regard this paper as an invitation to the academic community to engage in a discourse on issues underpinning this new understanding of the nature of research data.
Network analysis of Word of Mouth (WOM) examines how customers exchange opinions within their social networks. Compared to standard survey questions, which typically measure the likelihood to recommend, the network approach provides more metrics (e.g., average path length, clustering coefficient, density, average degree) that can be used to diagnose customer chatter. Unfortunately, traditional WOM has not benefitted from network analysis, which usually is applied to online WOM due to the availability of stored data. Despite the pervasiveness of online WOM, however, recent commercial reports reveal that traditional WOM still surpasses online WOM by a large margin. Traditional WOM also is perceived as more trustworthy and persuasive than online WOM. Considering the strong standing of traditional WOM and the advances in network analysis due to online WOM, this study fills a gap by demonstrating how a network analysis can be applied to traditional WOM. Network analysis is more demanding on the researcher and the respondents, but as the study illustrates, it also is more diagnostic than a standard survey. A preliminary study confirmed that people, indeed, are more likely to share traditional WOM then online WOM. The main study utilized network analysis by using an alter-alter survey method, which was used to map the network structures of a variety of WOM networks. Specifically, we examined the WOM networks structure as a function of product type (search, experience, and credence products) and opinion valence (positive vs. negative). The results reveal that WOM is affected primarily by product type. People are most likely to share opinions about experience products, followed by opinions about search products, and least likely to talk about credence products. The effect of opinion valence is limited. Practitioners can use these findings to manage WOM primarily based on product type by including search, experience, or credence qualities in promotional messages. This is the first study to compare WOM networks to the existing social network, which can serve as a benchmark for evaluating WOM campaigns. The results reveal that for most products, people do not utilize all of their social connections for WOM, but there are exceptions, such as sharing a positive opinion about a movie, where WOM chatter can exceed the social network. The study discusses the WOM network metrics from a practical perspective and how they can be used to optimize WOM campaigns. Overall, the conclusion is that network analysis is a viable technique for studying traditional WOM, which brings new research directions.
Abstract: Engaging the Grounded Theory Methodology (GTM) in International Relation (IR) studies can be a challenging choice for researchers and Ph.D. students. Considering that scholars in the IR field are familiar with certain traditional methodologies, the notion of importing a relatively unique approach such as the GTM can attract strong ontological and epistemological questions. In this article, I contend that pragmatist and constructivist versions of GTM can be a successful research methodology in IR qualitative research. Such choice, however, is constrained by a set of conditions. Guided by existing literature, these conditions were identified and discussed in the context of IR qualitative research norms and then applied in Foreign Policy (FP) decision-making process, a well-known approach in studying FP as a subdiscipline in IR. The article concludes that despite certain limitations, the methodology can be an outstanding option for IR qualitative research.
Threshold concepts are critical to student learning, providing gateways to understanding particular fields or disciplines. This paper adopts this idea of threshold concepts and relevant teaching and assessment practices to illustrate its use in postgraduate students' teaching and learning activities when developing a conceptual framework for their research. This paper addresses several key topics, namely: (1) How conceptual frameworks are introduced and explained; (2) Differentiating quantitative variance conceptual frameworks from qualitative process conceptual frameworks; (3) Explaining and illustrating how to conduct process theory in qualitative research using the case study method, grounded theory method, and critical incident technique; and (4) Illustrating the role of formative and summative assessment as a form of scaffolding in the teaching and learning process.
This article presents nine common challenges postgraduate students and early career academics face when engaging with academic literature. Data was collected from a sample of sixty-two postgraduate and early career academics who participated in a series of workshops on research methodology at a research-intensive university in New Zealand. Participants were invited to answer open-ended questions online about the purpose of undertaking a literature review and the challenges associated with the process. Findings revealed that participants held fragmented views about the purpose of engaging with the literature review, which contributed to the difficulties they faced in effectively undertaking the literature review. The challenges participants reported when undertaking literature reviews: difficulties in choosing a practical approach to reviewing the literature, inability to design an efficient search strategy to locate materials for review, problems locating relevant literature, an inability to determine the appropriate scope of a review, issues in choosing relevant materials and managing the growing volume of published work, problems in effectively synthesising and critiquing the literature, inability to organise and write clear reports, and lack of indicators for assessing the quality of written literature reports. The research presents a wide range of strategies students, and early career academics can use to mitigate these challenges. Teachers of research methods can also use these strategies to support students develop the necessary skills and knowledge to tackle the challenges of engaging with the literature.
