The relationship between the personality traits of entrepreneurs and their decision-making process: the role of manufacturing SMEs’ Institutional Environment in India

Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM)In view of its essential role in knowledge creation, multivariate data analysis prevails in the social sciences literature. The field of tourism is not an exception, specifically in the widely adoption of structural equation modeling (SEM), a multivariate technique, by tourism researchers over the past decade. While there are two major types of SEM including covariance-based SEM (CB-SEM) and variance-based SEM (PLS-SEM), the former dominated previous tourism research. However, increasing use of PLS-SEM in tourism research has been witnessed in recent years. This upward trend is likely to persist in the near future given the growing popularity of PLS-SEM in other social sciences domains like marketing, strategic management, and management information system, as specified in the preface of the book. Indeed, PLS-SEM, in relative to CB- SEM, provides more flexibility in handling of data. For instance, PLS-SEM is well-suited for accommodating small sample sizes and complex model, fortesting a model containing both formative and reflective constructs, and for handling single-item measures. To this end, the timely introduction of the book A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) helps tourism researchers stand at the front edge of the SEM technique and make effective use of the PLS-SEM in data analysis. Additionally, the book illustrates the application of PLS-SEM with a free downloadable software namely SmartPLS which is essential to extend the application of PLS-SEM in tourism research.Authored by Hair, Hult, Ringo, and Sarstedt, the book consists of eight chapters. To equip the readers with the basic knowledge of PLS- SEM, Chapter 1 delineates the meaning of SEM and its relationship with multivariate data analysis, followed by a description of the major elements in multivariate data analysis. Then the basic elements of PLS-SEM are explained. Finally, PLS-SEM is distinguished from its counterpart namely CB-SEM while the major characteristics of PLS-SEM and the conditions where the PLS-SEM are more adequate than CB-SEM and vice versa are discussed. To step in the application of PLS- SEM, Chapter 2 firstly explicates the concepts in structural model specification including mediation, moderation, and higher-order models. Then specification of measurement model is explained with a special focus on the differences between reflective and formative measures. After that, the issues that need to be addressed after data collection are discussed. The chapter ends by creating the model in the SmartPLS is illustrated. With an established model, Chapter 3 focuses on model estimation. The chapter explains the algorithm underpinning the estimation and the statistical properties of the PLS-SEM method, as well as the options and parameter settings for running the algorithm. Following that, the issues about interpretation of results are explained. The final section illustrates the execution of model estimation in the SmartPLS.Based on the model estimation, empirical measures of the measurement and structural models are derived, where evaluation of the models takes place. Chapter 4 exhibits the major steps in model evaluation in the beginning. Thereafter, the chapter explains the evaluation of reflective measurement models according to three major criteria including internal consistency reliability, convergent validity, and discriminant validity, followed by an illustration with the SmartPLS. Chapter 5 explains the assessment of formative measurement models with respect to the criteria of convergent validity, collinearity, and significance and relevance of the formative indicators. The chapter also elucidates the basic concepts of bootstrapping which is used to examine the statistical significance of estimates in PLS- SEM. An illustration of the assessment of formative measurement model in the SmartPLS follows. Chapter 6 continues the topic on model evaluation by focusing on the assessment of structural model. …

This empirical study examined links between entrepreneurial personality traits and perception of new venture opportunity in a sample of 207 respondents. Four entrepreneurial personality traits were included to predict respondents℉ perception of new venture opportunity. They are (1) achievement motivation, (2) locus of control, (3) risk propensity, and (4) proactivity.The results of multiple regression analysis show that three of the four entrepreneurial personality traits‐locus of control, risk propensity, and proactivity‐related significantly to perception of new venture opportunity in expected directions. Among the three personality traits, proactivity was found to have the strongest influence over entrepreneurial perception. No significant relationship was found between achievement motivation and perception of new venture opportunity. Among six control variables, only work experience was found to influence perception of new venture opportunity. This study explored links between entrepreneurial personalities and cognition and its results suggest that a combination of trait and cognition approaches contributes to a better understanding of entrepreneurial decision-making process. Both theoretical and practical implications were discussed.

Purpose– Structural equation modeling (SEM) has been widely used to examine complex research models in international business and marketing research. While the covariance-based SEM (CB-SEM) approach is dominant, the authors argue that the field’s dynamic nature and the sometimes early stage of theory development more often require a partial least squares SEM (PLS-SEM) approach. The purpose of this paper is to critically review the application of SEM techniques in the field.Design/methodology/approach– The authors searched six journals with an international business (and marketing) focus (Management International Review,Journal of International Business Studies,Journal of International Management,International Marketing Review,Journal of World Business,International Business Review) from 1990 to 2013. The authors reviewed all articles that apply SEM, analyzed their research objectives and methodology choices, and assessed whether the PLS-SEM papers followed the best practices outlined in the past.Findings– Of the articles, 379 utilized CB-SEM and 45 PLS-SEM. The reasons for using PLS-SEM referred largely to sampling and data measurement issues and did not sufficiently build on the procedure’s benefits that stem from its design for predictive and exploratory purposes. Thus, the procedure’s key benefits, which might be fruitful for the theorizing process, are not being fully exploited. Furthermore, authors need to better follow best practices to truly advance theory building.Research limitations/implications– The authors examined a subset of journals in the field and did not include general management journals that publish international business and marketing-related studies. Fur-thermore, the authors found only limited use of PLS-SEM in the journals the authors considered relevant to the study.Originality/value– The study contributes to the literature by providing researchers seeking to adopt SEM as an analytical method with practical guidelines for making better choices concerning an appropriate SEM approach. Furthermore, based on a systematic review of current practices in the international business and marketing literature, the authors identify critical challenges in the selection and use of SEM procedures and offer concrete recommendations for better practice.

In engineering design, the decision-making process holds significant importance as it plays an important role in determining the outcomes of a task. The decision-making process is notably influenced by various factors, with particular focus on the personality traits and information available. The purpose of this study is to comprehensively investigate the effects of these factors on quality and confidence in decision-making within the context of engineering design. To achieve this objective, we utilized a simulated design environment that can capture decision-making information. The analysis of personality traits was carried out utilizing the complete Big Five model, while the estimate of the structural equation model was executed by employing partial least squares structural equation modeling (PLS-SEM) and a machine learning model for quality estimation. The available empirical research indicates that individuals who have a lower degree of extraversion and agreeableness, and higher levels of conscientiousness and openness, are more likely to make decisions of higher quality. These characteristics have been found to have no significant effect on the levels of confidence during the process of making decisions. Furthermore, it was found that the trait of neuroticism has a negative impact on the quality of decision-making but does not have a significant impact on decision-making confidence. The noticeable finding was the strong impact of test-assessed knowledge on decision quality and confidence, in contrast to the lack of significant effect of self-assessed knowledge. This highlights the importance of carefully aligning tasks with individual personality traits in organizations working in the engineering design sector and prioritizing factual demonstrated knowledge rather than subjective self-assessment when assigning decision-making positions to individuals. These findings highlight the importance of considering personality traits and domain knowledge in educational and professional settings to enhance decision-making quality and confidence among engineering students, potentially informing targeted training and assessment practices.

PurposeStructural equation modeling (SEM) depicts one of the most salient research methods across a variety of disciplines, including hospitality management. Although for many researchers, SEM is equivalent to carrying out covariance-based SEM, recent research advocates the use of partial least squares structural equation modeling (PLS-SEM) as an attractive alternative. The purpose of this paper is to systematically examine how PLS-SEM has been applied in major hospitality research journals with the aim of providing important guidance and, if necessary, opportunities for realignment in future applications. Because PLS-SEM in hospitality research is still in an early stage of development, critically examining its use holds considerable promise to counteract misapplications which otherwise might reinforce over time.Design/methodology/approachAll PLS-SEM studies published in the six SSCI-indexed hospitality management journals between 2001 and 2015 were reviewed. Tying in with the prior studies in the field, the review covers reasons for using PLS-SEM, data characteristics, model characteristics, the evaluation of the measurement models, the evaluation of the structural model, reporting and use of advanced analyses.FindingsCompared to other fields, the results show that several reporting practices are clearly above standard but still leave room for improvement, particularly regarding the consideration of state-of-the art metrics for measurement and structural model assessment. Furthermore, hospitality researchers seem to be unaware of the recent extensions of the PLS-SEM method, which clearly extend the scope of the analyses and help gaining more insights from the model and the data. As a result of this PLS-SEM application review in studies, this research presents guidelines on how to accurately use the method. These guidelines are important for the hospitality management and other disciplines to disseminate and ensure the rigor of PLS-SEM analyses and reporting practices.Research limitations/implicationsOnly articles published in the SSCI-indexed hospitality journals were examined and any journals indexed in other databases were not included. That is, while this research focused on the top-tier hospitality management journals, future research may widen the scope by considering hospitality management-related studies from other disciplines, such as tourism research or general management.Originality/valueThis study contributes to the literature by providing hospitality researchers with the updated guidelines for PLS-SEM use. Based on a systematic review of current practices in the hospitality literature, critical methodological issues when choosing and using the PLS-SEM were identified. The guidelines allow to improve future PLS-SEM studies and offer recommendations for using recent advances of the method.

PurposeThis study investigates how the Big Five (BF) personality traits influence green consumerism (GCM), focusing on the extent to which each trait predicts eco-friendly behaviors. It also explores the relative importance of these traits using both partial least squares structural equation modeling (PLS-SEM) and artificial neural networks (ANN).Design/methodology/approachThis study uses a quantitative approach, surveying 689 respondents through a structured questionnaire. ANN was utilized to complement PLS-SEM and to validate the significance of antecedents identified via PLS-SEM, thereby improving the robustness and practical relevance of the findings.FindingsResults from both PLS-SEM and ANN revealed that extraversion (EXT) was the most significant predictor of GCM, followed by conscientiousness (CON), agreeableness, openness to experience and neuroticism (NEU). While EXT had the greatest influence, NEU negatively impacted GCM.Practical implicationsMarketers can tailor green campaigns by targeting individuals with high EXT and CON, emphasizing the social and ethical dimensions of green products.Social implicationsUnderstanding personality-based drivers of green behavior helps promote sustainable consumption patterns, contributing to environmental protection and social responsibility.Originality/valueThis research contributes by integrating PLS-SEM and ANN, offering a novel approach to understanding the influence of BF personality traits on GCM.

ABSTRACTAs generative artificial intelligence (GenAI) increasingly penetrates language education, understanding learners' continued intention to use this technology becomes crucial. This study examines EFL learners' continuance intention to use GenAI for language learning through PLS‐SEM and fsQCA methodologies. Participants were undergraduate EFL learners (N = 383) from three universities in Taiwan, aged 18–24, with varying English proficiency levels (TOEIC 450–860 or CEFR A2‐B2). Data were collected through an online survey comprising validated scales measuring technology acceptance constructs and personality traits using 5‐point Likert scales. Analysis employed both partial least squares structural equation modelling (PLS‐SEM) and fuzzy‐set qualitative comparative analysis (fsQCA). Results showed that three key dimensions – dependability, attractiveness and novelty – shape users' overall experience with GenAI, where dependability and attractiveness specifically align with users' initial expectations. Both expectancy confirmation and social influence predict learners' continuance intention. Openness to experience moderates these relationships positively for expectancy confirmation but negatively for social influence, indicating that individuals with high openness prioritise personal evaluation over social cues. As the first study to combine PLS‐SEM with fsQCA in GenAI use research, it reveals complementary insights: while PLS‐SEM identifies key predictors of continued use, fsQCA uncovers multiple pathways to adoption, with dependability and social influence as core conditions across configurations. This dual‐method approach advances theoretical understanding of technology adoption by demonstrating how linear relationships coexist with complex configurational patterns. The findings provide practical implications for educational technology design, emphasising the need to balance system reliability and aesthetic appeal while accounting for individual differences in user personality traits.

Partial least squares structural equation modeling (PLS-SEM) is a multivariate statistical technique that helps examine complex relationships among a number of variables. Although its use has increased over decades, PLS-SEM remains underutilized in leisure research. The purpose of this methodological paper is to offer a primer on PLS-SEM for leisure researchers and to present a critical review of PLS-SEM’s strengths and limitations, while identifying potential applications of PLS-SEM across different sub-fields and theories in leisure research. Specifically, as to strengths, we discuss PLS-SEM’s sample size requirements, accommodation of formative and reflective measures, ability to model many variables and relationships, and statistical prediction capacity. In terms of its limitations, we review criticisms regarding PLS-SEM’s biased estimates as well as the lack of measurement error estimation and model fit assessment tools. Lastly, we provide recommendations for leisure researchers who wish to use PLS-SEM and journal editors and reviewers who assess PLS-SEM articles.

Partial Least Squares Structural Equation Modeling (PLS-SEM) in second language and education research: Guidelines using an applied example

PurposeThe purpose of this paper is to analyze the existing project management literature by conceptualizing the influence of personality and cognitive traits on project managers’ risk-taking behaviour.Design/methodology/approachThe paper is based on an in-depth analysis of the existing literature to develop framework for conceptualizing risk propensity in project management.FindingsThe results indicate that the Big Five personality traits cannot capture risk propensity in risk-taking behaviour on their own. Cognitive traits are indispensable components in risk propensity.Research limitations/implicationsThe paper examines the association between risk propensity theories and personality traits. The paper framed project managers’ personality traits that can impact their tendency to take risky decisions, that is risk propensity.Originality/valueThis paper expands literature by increasing our understanding of personality and cognitive traits in risk propensity.

Online digital market platform business model designers, marketers, and retailers can further expand their marketing strategies to draw in and keep customers to gain a competitive edge globally if they are aware of the elements influencing consumers' purchasing intentions. The purpose of this research is to identify the crucial variables impacting Addis Ababa University, Graduating Engineering Students’ desire to purchase on online digital market platforms, and narrow the research gap on determinants of online purchase intention of Ethiopian consumers. This study adopted a descriptive and inferential survey design, epistemology assumption, and employed the positivism research philosophy approach to test the research hypotheses. The primary study technique used to collect relevant data was a closed-ended 5-point Likert scale questionnaire. The information was gathered from 100 Ethiopian, Addis Ababa University, graduating engineering students. With the use of SPSS version 23 and SmartPLS version 3.0 software, the data were examined using descriptive statistics and the inferential partial least square structural equation modeling (PLS-SEM) technique. The results of this study highlighted five useful decision-making elements that have an impact on the selected consumers' intention to buy on online digital market platforms including Website Design, Perceived Usefulness, Perceived Ease of Use, Trust, and Subjective Norms. The Practical Implication of this research is that with a clear understanding of the key determinants of consumers’ purchase intention on online digital market platforms; manufacturers, online marketers, and retailers can create effective market strategies, enhance technology, and make smart marketing choices that will help them gain global competitive advantage. This study is unique in that it uses a new conceptual research framework and the partial least square structural equation modeling (PLS-SEM) technique to analyze relationships between determinant variables and consumers’ intention to purchase on online digital market platforms. The major finding of this research provides empirical evidence towards the key determinant variables of consumers’ purchase intention on online digital market platforms. The small sample size is one of the limitations to generalize the finding of this research. Future studies should focus on enlarging the sample size and assessing more determinant variables to get a generalizable result.

PurposePartial least squares structural equation modeling (PLS-SEM) has become an established social sciences multivariate analysis technique. Since quality management researchers also increasingly using PLS-SEM, this growing interest calls for guidance.Design/methodology/approachBased on established guidelines for applying PLS-SEM and evaluating the results, this research reviews 107 articles applying the method and published in eight leading quality management journals.FindingsThe use of PLS-SEM in quality management often only draws on limited information and analysis results. The discipline would benefit from the method's more comprehensive use by following established guidelines. Specifically, the use of predictive model assessment and more advanced PLS-SEM analyses harbors the potential to provide more detailed findings and conclusions when applying the method.Research limitations/implicationsThis research provides first insights into PLS-SEM's use in quality management. Future research should identify the key areas and the core quality management models that best support the method's capabilities and researchers' goals.Practical implicationsThe results of this analysis guide researchers who use the PLS-SEM method for their quality management studies.Originality/valueThis is the first article to systematically review the use of PLS-SEM in the quality management discipline.

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Partial least squares structural equation modelling (PLS-SEM) is becoming a popular statistical framework in many fields and disciplines of the social sciences. The main reason for this popularity is that PLS-SEM can be used to estimate models including latent variables, observed variables, or a combination of these. The popularity of PLS-SEM is predicted to increase even more as a result of the development of new and more robust estimation approaches, such as consistent PLS-SEM. The traditional and modern estimation methods for PLS-SEM are now readily facilitated by both open-source and commercial software packages. This book presents PLS-SEM as a useful practical statistical toolbox that can be used for estimating many different types of research models. In so doing, the authors provide the necessary technical prerequisites and theoretical treatment of various aspects of PLS-SEM prior to practical applications. What makes the book unique is the fact that it thoroughly explains and extensively uses comprehensive Stata (plssem) and R (cSEM and plspm) packages for carrying out PLS-SEM analysis. The book aims to help the reader understand the mechanics behind PLS-SEM as well as performing it for publication purposes. Features: Intuitive and technical explanations of PLS-SEM methods Complete explanations of Stata and R packages Lots of example applications of the methodology Detailed interpretation of software output Reporting of a PLS-SEM study Github repository for supplementary book material The book is primarily aimed at researchers and graduate students from statistics, social science, psychology, and other disciplines. Technical details have been moved from the main body of the text into appendices, but it would be useful if the reader has a solid background in linear regression analysis.

The study investigates the relationship between entrepreneur’s personality characteristics, i.e. need for achievement and internal locus of control, with the process of start-up. Besides, this study examines the role of risk propensity as a mediator in the relationship between personality characteristics and the process of start-up. Also, this study assesses the interaction effects of institutional environment on the relationship between risk propensity and the process of start-ups. Data were collected through a single cross-sectional survey from 478 entrepreneurs in India. The study used partial least squares approach to path modelling to examine the proposed relationship in the research model. After controlling the effects of risk propensity, the results reveal internal locus of control and need for achievement are significantly related to start-up process. Furthermore, the relationship between risk propensity and start-up was moderated by institutional environment. Findings of the study indicate the importance of personal characteristics in the process of start-up. The study also emphasizes how the institutional environment enhances the level of the process of start-up. Moreover, this study is useful in differentiating personality characteristics from non-entrepreneurs.