Enhancing Global Sales Skills in Executive Education Programs

The purpose of this article is to examine the need for executive education training in culture, suggest a method for clarifying situations where cultural training is needed, and provide guidelines on the content of executive education training programs for companies pursuing global opportunities

Mktg

A new approach to learning the principles of marketing, MKTG is the Asia–Pacific edition of a proven, innovative solution to enhance the students' learning experience. Concise, yet complete, coverage supported by a suite of online learning aids equips students with the tools required to successfully undertake an introductory marketing course. Paving a new way to both teaching and learning, MKTG is designed to truly connect with today's busy tech-savy student. Students have access to online interactive quizzing, videos, podcasts, flashcards, marketing plans, games and more. An accessible, easy-to-read text along with tear out review cards complete a package which helps students to learn important concepts faster

Integrating the Expanded Task-technology Fit Theory and the Technology Acceptance Model: A Multi-wave Empirical Analysis

Task-technology fit theory proposes that the match between tasks and technologies, known as task-technology fit, has a positive relation with technology use and performance. Researchers have recently extended task-technology fit theory by conceptualizing task-technology misfit, which describes instances in which technology provides too few (too little) or too many (too much) features to perform a task. We link this newly expanded theory, which we label expanded task- technology fit (E-TTF) theory, with the technology acceptance model (TAM). We conducted a study and found that task- technology fit and too little significantly related to the variables in the TAM and that each ultimately had an indirect effect on use. In contrast, too much did not significantly relate to any variable in the TAM. These results support that E-TTF theory explains meaningful variance in the TAM, which suggests that integrating these theories is important for understanding technology use. Likewise, these results emphasize the importance of the multidimensional conceptualization that the E-TTF theory proposes. Too little (too few features) predicted outcomes beyond task- technology fit and meaningfully improved our model’s predictive abilities. In contrast, too much’s (too many features) relationships lacked significance, which emphasizes the need to distinguish types of task-technology misfit. Therefore, our study provides benefits for research on E-TTF theory, the TAM, and their integration

Extraordinary Claims Require Extraordinary Evidence: A Comment on “Recent Developments in PLS”

Evermann and Rönkkö (2023) review recent developments in partial least squares (PLS) with the aim of providing guidance to researchers. Indeed, the explosion of methodological advances in PLS in the last decade necessitates such overview articles. In so far as the goal is to provide an objective assessment of the technique, such articles are most welcome. Unfortunately, the authors’ extraordinary and questionable claims paint a misleading picture of PLS. Our goal in this short commentary is to address selected claims made by Evermann and Rönkkö (2023) using simulations and the latest research. Our objective is to bring a positive perspective to this debate and highlight the recent developments in PLS that make it an increasingly valuable technique in IS and management research in general

How to specify, estimate, and validate higher-order constructs in PLS-SEM

Higher-order constructs, which facilitate modeling a construct on a more abstract higher-level dimension and its more concrete lower-order subdimensions, have become an increasingly visible trend in applications of partial least squares structural equation modeling (PLS-SEM). Unfortunately, researchers frequently confuse the specification, estimation, and validation of higher-order constructs, for example, when it comes to assessing their reliability and validity. Addressing this concern, this paper explains how to evaluate the results of higher-order constructs in PLS-SEM using the repeated indicators and the two-stage approaches, which feature prominently in applied social sciences research. Focusing on the reflective-reflective and reflective-formative types of higher-order constructs, we use the well-known corporate reputation model example to illustrate their specification, estimation, and validation. Thereby, we provide the guidance that scholars, marketing researchers, and practitioners need when using higher-order constructs in their studies

Development and validation of attitudes measurement scales: fundamental and practical aspects

Purpose – This paper aims to present the fundamental aspects for the development and validation (D&V) of attitudes’ measurement scale, as well as its practical aspects that are not deeply explored in books and manuals. These aspects are the results of a long experience of the authors and arduous learning with errors and mistakes. Design/methodology/approach – The nature of this paper is methodological and can be very useful for an initial reading on the theme that it rests. This paper presents four D&V stages: literature review or interviews with experts; theoretical or face validation; semantic validation or validation with possible respondents; and statistical validation. Findings – This is a methodological paper, and its main finding is the usefulness for researchers. Research limitations/implications – The main implication of this paper is to support researchers on the process of D&V of measurement scales. Practical implications – Became a step-by-step guide to researchers on the D&V of measurement scales. Social implications – Support researchers on their data collection and analysis. Originality/value – This is a practical guide, with tips from seasoned scholars to help researchers on the D&V of measurement scale

Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R

Partial least squares structural equation modeling (PLS-SEM) has become a standard approach for analyzing complex inter-relationships between observed and latent variables. Researchers appreciate the many advantages of PLS-SEM such as the possibility to estimate very complex models and the method’s flexibility in terms of data requirements and measurement specification. This practical open access guide provides a step-by-step treatment of the major choices in analyzing PLS path models using R, a free software environment for statistical computing, which runs on Windows, macOS, and UNIX computer platforms. Adopting the R software’s SEMinR package, which brings a friendly syntax to creating and estimating structural equation models, each chapter offers a concise overview of relevant topics and metrics, followed by an in-depth description of a case study. Simple instructions give readers the “how-tos” of using SEMinR to obtain solutions and document their results. Rules of thumb in every chapter provide guidance on best practices in the application and interpretation of PLS-SEM

Addressing Endogeneity in International Marketing Applications of Partial Least Squares Structural Equation Modeling

Partial least squares structural equation modeling (PLS-SEM) has become a key method in international marketing research. Users of PLS-SEM have, however, largely overlooked the issue of endogeneity, which has become an integral component of regression analysis applications. This lack of attention is surprising because the PLS-SEM method is grounded in regression analysis, for which numerous approaches for handling endogeneity have been proposed. To identify and treat endogeneity, and create awareness of how to deal with this issue, this study introduces a systematic procedure that translates control variables, instrumental variables, and Gaussian copulas into a PLS-SEM framework. We illustrate the procedure's efficacy by means of empirical data and offer recommendations to guide international marketing researchers on how to effectively address endogeneity concerns in their PLS-SEM analyses

Prediction: coveted, yet forsaken? Introducing a cross-validated predictive ability test in partial least squares path modeling

Management researchers often develop theories and policies that are forward‐looking. The prospective outlook of predictive modeling, where a model predicts unseen or new data, can complement the retrospective nature of causal‐explanatory modeling that dominates the field. Partial least squares (PLS) path modeling is an excellent tool for building theories that offer both explanation and prediction. A limitation of PLS, however, is the lack of a statistical test to assess whether a proposed or alternative theoretical model offers significantly better out‐of‐sample predictive power than a benchmark or an established model. Such an assessment of predictive power is essential for theory development and validation, and for selecting a model on which to base managerial and policy decisions. We introduce the cross‐validated predictive ability test (CVPAT) to conduct a pairwise comparison of predictive power of competing models, and substantiate its performance via multiple Monte Carlo studies. We propose a stepwise predictive model comparison procedure to guide researchers, and demonstrate CVPAT's practical utility using the well‐known American Customer Satisfaction Index (ACSI) model

Replaced by a robot: Service implications in the age of the machine

Service organizations, emboldened by the imperative to innovate, are increasingly introducing robots to frontline service encounters. However, as they augment or substitute human employees with robots, they may struggle to convince a distrusting public of their brand’s ethical credentials. Consequently, this article develops and tests a holistic framework to ascertain a deeper understanding of customer perceptions of frontline service robots (FLSRs) than has previously been attempted. Our experimental studies investigate the effects of the (1) role (augmentation or substitution of human employees or no involvement) and (2) type (humanoid FLSR vs. self-service machine) of FLSRs under the following service contexts: (a) value creation model (asset-builder, service-provider) and (b) service type (experience, credence). By empirically establishing our framework, we highlight how customers’ personal characteristics (openness-to-change and preference for ethical/responsible service provider)  and cognitive evaluations (perceived innovativeness, perceived ethical/societal reputation, and perceived innovativeness-responsibility fit) influence the impact that FLSRs have on service experience and brand usage intent. Our findings operationalize and empirically support seminal frameworks from extant literature, as well as elaborate on the positive and negative implications of using robots to complement or replace service employees. Further, we consider managerial and policy implications for service in the age of machines