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The purpose of this paper is to theoretically develop and empirically validate separate scales that represent a consumer’s expectations of business ethics (BE) and corporate social responsibility (CSR).

A literature review and qualitative research were conducted to generate items for the scales. Initial item reduction was performed qualitatively based on a panel of experts. A follow-up quantitative assessment using an exploratory factor analysis further reduced the items. The scales were then validated using confirmatory composite analysis with partial least squares-structural equation modeling.

Separate scales representing consumers’ expectations of BE and CSR behaviors were developed. The scales exhibited reliability, convergent validity, discriminant validity and external validity.

The separation of these scales into two components will facilitate more precise examination of consumer perceptions of these two components of product and brand images, and how they may impact brand attitudes and brand trust.

This is the first effort to develop separate scales for consumer expectations of ethics and CSR, and assess their impact on brand outcomes.

The purpose of this study is to provide an overview of emerging prediction assessment tools for composite-based PLS-SEM, particularly proposed out-of-sample prediction methodologies.

A review of recently developed out-of-sample prediction assessment tools for composite-based PLS-SEM that will expand the skills of researchers and inform them on new methodologies for improving evaluation of theoretical models. Recently developed and proposed cross-validation approaches for model comparisons and benchmarking are reviewed and evaluated.

The results summarize next-generation prediction metrics that will substantially improve researchers' ability to assess and report the extent to which their theoretical models provide meaningful predictions. Improved prediction assessment metrics are essential to justify (practical) implications and recommendations developed on the basis of theoretical model estimation results.

The paper provides an overview of recently developed and proposed out-of-sample prediction metrics for composite-based PLS-SEM that will enhance the ability of researchers to demonstrate generalization of their findings from sample data to the population.

The authors aim to present partial least squares (PLS) as an evolving approach to structural equation modeling (SEM), highlight its advantages and limitations and provide an overview of recent research on the method across various fields.

In this review article, the authors merge literatures from the marketing, management, and management information systems fields to present the state-of-the art of PLS-SEM research. Furthermore, the authors meta-analyze recent review studies to shed light on popular reasons for PLS-SEM usage.

PLS-SEM has experienced increasing dissemination in a variety of fields in recent years with nonnormal data, small sample sizes and the use of formative indicators being the most prominent reasons for its application. Recent methodological research has extended PLS-SEM's methodological toolbox to accommodate more complex model structures or handle data inadequacies such as heterogeneity.

While research on the PLS-SEM method has gained momentum during the last decade, there are ample research opportunities on subjects such as mediation or multigroup analysis, which warrant further attention.

This article provides an introduction to PLS-SEM for researchers that have not yet been exposed to the method. The article is the first to meta-analyze reasons for PLS-SEM usage across the marketing, management, and management information systems fields. The cross-disciplinary review of recent research on the PLS-SEM method also makes this article useful for researchers interested in advanced concepts.

Halal food endorsements perceived positively by the focal target group may lead to a negative reaction of consumers that harbor animosity against this target group. For such potentially controversial endorsements, in-group animosity against out-group associated product endorsements could lead to a rejection and even an outspoken disapproval of these food products. The purpose of this paper is to explain what drives in-group reactions to Halal endorsements.

The authors use Social Identity Theory and the Social Dominance Theory in explaining animosity toward out-groups and willingness to buy products with Halal endorsements. Specifically, the authors analyze the reaction of more than 800 in-group majority Christians toward out-group minority Muslim directed Halal endorsements. Following the development of hypotheses and a conceptual model, structural equation modeling is used to measure the relationships between the constructs.

Constructs based on Social Dominance Theory and Social Identity Theory predict animosity toward out-group endorsements, but the relationship between Social Dominance Theory and animosity is much stronger. Animosity is a mediator between these two constructs and willingness to purchase products with out-group focussed endorsements (Halal).

The research has been conducted in one particular country (Austria) and focusses on a specific type of controversial endorsement, namely a religious (Halal) endorsement. Other research contexts (i.e. other countries and/or different types of controversial endorsements) should be used to widen the empirical base and validate the findings.

Marketers should be aware of a possible negative impact of out-group focussed endorsements. In particular, they should be cognizant that racism and ethnocentrism prevailing in a society could reduce the purchase intent of in-groups.

Efforts are required to combat the drivers of animosity between in-groups and out-groups. This paper provides insights on how this may be achieved.

This study focusses on a hitherto neglected phenomenon, i.e. controversial endorsements. It uses two alternative theories and advances the understanding of the role of animosity in a domestic consumer setting; an issue that has nearly exclusively been discussed with regard to cross-border purchasing.

The purpose of this paper is to provide an overview of unobserved heterogeneity in the context of partial least squares structural equation modeling (PLS-SEM), its prevalence and challenges for social science researchers. Part II – in the next issue (European Business Review, Vol. 28 No. 2) – presents a case study, which illustrates how to identify and treat unobserved heterogeneity in PLS-SEM using the finite mixture PLS (FIMIX-PLS) module in the SmartPLS 3 software.

The paper merges literatures from various disciplines, such as management information systems, marketing and statistics, to present a state-of-the-art review of FIMIX-PLS. Based on this review, the paper offers guidelines on how to apply the technique to specific research problems.

FIMIX-PLS offers a means to identify and treat unobserved heterogeneity in PLS-SEM and is particularly useful for determining the number of segments to extract from the data. In the latter respect, prior applications of FIMIX-PLS restricted their focus to a very limited set of criteria, but future studies should broaden the scope by considering information criteria, theory and logic.

Since the introduction of FIMIX-PLS, a range of alternative latent class techniques have emerged to address some of the limitations of the approach relating, for example, to the technique’s inability to handle heterogeneity in the measurement models and its distributional assumptions. The second part of this article (Part II) discusses alternative latent class techniques in greater detail and calls for the joint use of FIMIX-PLS and PLS prediction-oriented segmentation.

This paper is the first to offer researchers who have not been exposed to the method an introduction to FIMIX-PLS. Based on a state-of-the-art review of the technique in Part I, Part II follows up by offering a step-by-step tutorial on how to use FIMIX-PLS in SmartPLS 3.

The purpose of this paper is to provide an overview of predictive analytics, summarize how it is impacting knowledge creation in marketing, and suggest future developments in marketing and predictive analytics for both organizations and researchers.

Survival in a knowledge‐based economy is derived from the ability to convert information to knowledge. To do so, researchers and managers increasingly are relying on the field of predictive analytics. Data mining identifies and confirms relationships between explanatory and criterion variables. Predictive analytics uses confirmed relationships between variables to predict future outcomes. The predictions are most often values suggesting the likelihood a particular behavior or event will take place in the future.

Data mining and predictive analytics are increasingly popular because of the substantial contributions they can make in converting information to knowledge. Marketing is among the most frequent applications of the techniques, and whether you think about product development, advertising, distribution and retailing, or marketing research and business intelligence, data mining and predictive analytics increasingly are being applied.

In the future, we can expect predictive analytics to increasingly be applied to databases in all fields and revolutionize the ability to identify, understand and predict future developments, data analysts will increasingly rely on mixed‐data models that examine both structured (numbers)and unstructured (text and images) data, statistical tools will be more powerful and easier to use, future applications will be global and real time, demand for data analysts will increase as will the need for students to learn data analysis methods, and scholarly researchers will need to improve their quantitative skills so the large amounts of information available can be used to create knowledge instead of information overload.

The presence of securities crowdfunding (SCF) FinTech in the Islamic financial landscape opens investment opportunities through shares and sukuk (Sharia bond) instruments. This study aims to examine the effect of investment risk (IR), legal risk (LR), product knowledge (PK), Sharia compliance (SC) and subjective norm (SN) on investment decisions in businesses and projects run by small and medium enterprises (SMEs).

The questionnaires were distributed to prospective investors with prior knowledge of SCF and Islamic investment. The data collected was then examined using partial least square-structural equation modeling using SmartPLS 4.0.

The results show that LR has positive and significant implications for supporting investment through SCF, while IR has the opposite. The main findings in this study explain that PK and SC are proven to strengthen the intention to invest in SCF. Meanwhile, SN, which also strengthens intention, is the greatest influence. Therefore, it is highly recommended that SCF organizers collaborate with regulators (OJK), universities, academics and the investor community, as well as Muslim entrepreneurs, to provide education and literacy regarding SCF products and the underlying contracts, along with the consequences and uniqueness of investment vis SCF.

From a managerial side, Sharia expert educators can be appointed to increase investors’ literacy and confidence to support SMEs’ business expansion via SCF. In addition, to minimize investment risk, SCF organizers are also advised to issue sukuk and shares in different low-risk businesses/sectors, followed by investment amounts that are more affordable for novice investors.

Research on SCF as an alternative to SME financing is still scarce. To the best of the author’s knowledge, this is the first research to empirically test the relationship between risk, SC, PK and SN on potential investors’ decisions to support SMEs through the SCF mechanism.