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Social media brand platforms have become a popular means for engaged customers to share information and experiences with brands and other customers. However, empirical research on how customer engagement (CE) relates to customers’ sharing intentions with the brand is limited. This study aims to investigate causal patterns of four CE dimensions – focused attention, absorption, enthusiasm and interaction – together with two cognitive structure properties in stimulating sharing intentions with the brand.

Using data from 782 Chinese customers of brand pages on the social media platform Weibo, this paper is the first to use both finite mixture partial least squares (FIMIX-PLS) analysis and fuzzy-set qualitative comparative analysis (fsQCA) to empirically assess the impact of CE configurations on sharing intentions.

The findings imply that not all of the CE dimensions co-occur necessarily and that different configurations of them can produce superior sharing intentions, conditional on the cognitive structure of customers, including their level of brand knowledge and avant-gardism.

Although restricted to customers on Weibo, the results inform practice about how social media technology can facilitate different CE configurations and customer sharing intentions.

The results inform brand managers’ segmentation efforts and CE content marketing initiatives that can induce different CE configurations and customer sharing intentions with customers that possess high avant-gard and brand knowledge characteristics.

This study is the first to substantiate how different CE configurations (as gestalts) affect sharing intentions in social media and to challenge conventional net-effects thinking about CE dimensions. Understanding how such conditional configurations foster sharing via a social media platform is advantageous because it can improve segmentation efforts to strengthen brand relationships.

The purpose of this paper is to provide the results from empirically testing the effects of different combinations of organizational forms and combinative capabilities on the efficiency, scope and flexibility of firm‐level knowledge integration, given the influence of knowledge types and forms.

The research is based on a case‐study methodology employed to collect data from ten firms of low, medium and high knowledge complexity environs; manual and automated data mining techniques were employed.

The findings suggest that organizational form and combinative capabilities explain the effects of efficiency, scope and flexibility on firm‐level knowledge integration. In turn, differences in knowledge types and forms necessitate the use of secondary combinative capabilities.

While the study provides a coherent and detailed understanding of firm‐level knowledge integration and explain the development of a firm's knowledge architecture through organizational structures and synthesize existing literature contributing to an emergent understanding of the ambiguities surrounding combinative capabilities, further research identifying the effects of and relationship with the deep knowledge in combinative capabilities on strategic capabilities and a firm's knowledge vision would be beneficial.

Of practical relevance is the strategic and operational management implications detailing the specific organizational structures to achieve desired firm‐level knowledge integration capacity and manage particular integration efficiency, scope and flexibility requirements to enhance the development of architectural knowledge and, thus, firm capabilities.

The original contribution of this paper is reflected in providing empiric and theoretic insights, which directly address the specific combinations of organizational structures that influence integration process characteristics and thus accommodate differences in knowledge types and forms.

Purpose – Revisiting Fornell et al.'s (1996) seminal study, this chapter looks at the evidence for observed and unobserved heterogeneity within data underlying the American customer satisfaction index (ACSI) model. Examining data for two specific industries (utilities and hotels) reveals only modest differences. However, we suppose that unobserved heterogeneity critically affects the results. These insights provide the basis for shaping further differentiated ACSI model analyses and more precise interpretations.

Methodology/approach – This study applies the partial least squares (PLS) path modeling method and uses empirical data to estimate and compare the ACSI model results on the aggregate and industry-specific data levels. In addition, the finite mixture PLS path modeling (FIMIX-PLS) method is employed to further examine across industry similarities and within industry differences.

Findings – This research uncovers unobserved heterogeneity that guides forming three segments of customers within each industry. The major segment in each industry represents customers that are fairly loyal (i.e., neither disloyal nor extremely loyal) while the other two smaller segments are not as similar across the two industries. Our study identifies substantial differences across these segments within each industry. An importance-performance map analysis illustrates these differences and provides the basis for managerial implications.

Originality/value of the chapter – The unobserved heterogeneity revealed within industries in a given country (i.e., the United States of America) underlines the need to be open to differences within populations, beyond the observed heterogeneity across distinct groups or cultures, and the need to reconsider reporting requirements in academic research.

The purpose of this paper is to explore the knowledge infrastructure of methodological research on partial least squares structural equation modeling (PLS-SEM) from a network point of view. The analysis involves the structures of authors, institutions, countries and co-citation networks, and discloses trending developments in the field.

Based on bibliometric data downloaded from the Web of Science, the authors apply various social network analysis (SNA) and visualization tools to examine the structure of knowledge networks of the PLS-SEM domain. Specifically, the authors investigate the PLS-SEM knowledge network by analyzing 84 methodological studies published in 39 journals by 145 authors from 106 institutions.

The analysis reveals that specific authors dominate the network, whereas most authors work in isolated groups, loosely connected to the network’s focal authors. Besides presenting the results of a country level analysis, the research also identifies journals that play a key role in disseminating knowledge in the network. Finally, a burst detection analysis indicates that method comparisons and extensions, for example, to estimate common factor model data or to leverage PLS-SEM’s predictive capabilities, feature prominently in recent research.

Addressing the limitations of prior systematic literature reviews on the PLS-SEM method, this is the first study to apply SNA to reveal the interrelated structures and properties of PLS-SEM’s research domain.

“Garbage in, garbage out” is a common expression that academics and practitioners use to emphasize that empirical analysis is only as good as the basis on which it relies. Although the importance of sound data and valid measures has long been acknowledged, it is nevertheless often problematic to follow required quality standards in concrete research situations. Potential sources of error are usually unknown, methods to ensure data quality are unavailable, and existing methods for scale development, index construction, data collection, and data analysis are insufficient or erroneously applied. This is especially true of international marketing research, which often makes great demands on the data and measures used, as well as on the research methodology applied. Against this background, this volume addresses issues pertaining to measurement and research methodology in an international marketing context. Thanks to the efforts of authors and reviewers, we are pleased to present nine articles that deal with cutting-edge topics such as formative measurement, response-bias in cross-cultural research, marketing efficiency measurement, and segmentation methods.

The purpose of this paper is to investigate the effects of a firm’s service-dominant orientation on marketing and technological capabilities, and its performance. It outlines how a service-dominant orientation offers guidance for the development and deployment of ordinary capabilities, and indirectly affects performance. Additionally, it delineates how dynamic capabilities affect the impact of a service-dominant orientation on ordinary capabilities.

Partial least squares structural equation modeling drawing on data from 228 firms serves to assess hypotheses relating service-dominant orientation and dynamic capabilities with firm performance.

The results indicate that marketing and technological capabilities fully mediate the relationship between a firm’s service-dominant orientation and firm performance. Furthermore, the positive marginal effect of a firm’s service-dominant orientation on its marketing capabilities increases with the firm displaying a stronger service-dominant orientation. In addition, the positive effect of service-dominant orientation on marketing capabilities reduces the more the firm deploys dynamic capabilities.

Because of the cross-sectional sample, future studies could adopt longitudinal research designs to explore the impact of a service-dominant orientation on ordinary capabilities and performance, or investigate the applicability of the findings in other contexts.

The findings imply that implementing a service-dominant orientation can be beneficial for firms. However, because the impact of such an orientation weakens the greater a firm’s dynamic capabilities, managers need to be mindful of this trade-off.

The study is the first to establish a link between the dynamic capability view, originating from strategy research, and service-dominant logic, stemming from marketing thinking.

The aim of this study is to (1) construct a standard framework for assessing the capability of bioenergy enterprises' digital green innovation partners; (2) quantify the choice of partners for digital green innovation by bioenergy enterprises; (3) propose based on a dual combination empowerment niche digital green innovation field model.

Fuzzy set theory is combined into field theory to investigate resource complementarity. The successful application of the model to a real case illustrates how the model can be used to address the problem of digital green innovation partner selection. Finally, the standard framework and digital green innovation field model can be applied to the practical partner selection of bioenergy enterprises.

Digital green innovation technology of superposition of complementarity, mutual trust and resources makes the digital green innovation knowledge from partners to biofuels in the enterprise. The index rating system included eight target layers: digital technology innovation level, bioenergy technology innovation level, bioenergy green level, aggregated digital green innovation resource level, bioenergy technology market development ability, co-operation mutual trust and cooperation aggregation degree.

This study helps to (1) construct the evaluation standard framework of digital green innovation capability based on the dual combination empowerment theory; (2) develop a new digital green innovation domain model for bioenergy enterprises to select digital green innovation partners; (3) assist bioenergy enterprises in implementing digital green innovation practices.

This study aims to advance understanding about quality management (QM) practices by clarifying how competitive strategy conditions the impacts of exploitative and explorative QM practices on performance.

The authors apply partial least squares structural equation modeling to data from a sample of German pharmaceutical firms.

The results show that the impact of exploitative and explorative QM practices on firm performance is contingent on the competitive strategy pursued. Explorative QM practices are significantly more relevant for firms following a differentiation strategy, whereas exploitative QM practices are significantly more relevant for cost leaders. Furthermore, for strategically ambidextrous firms that follow simultaneously a cost and a differentiation focus, the interplay of the two QM practices matters.

This paper contributes to understanding which kind of management practices, exploitative and/or explorative, have greater performance impacts under certain competitive strategy conditions.