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Roemer, E., & Henseler, J. (2022). The dynamics of electric vehicle acceptance in corporate fleets: Evidence from Germany. Technology in Society, 68, 1-11. [101938]. https://doi.org/10.1016/j.techsoc.2022.101938 --- Funding Information: We would like to thank Elina Petersone, Lukas Burs, Jannis Lux and Mathias Musiol for their support in this research project. Moreover, we thank Ulya Faupel and Sarah Lahmeyer for their constructive comments on earlier versions of the paper. The data collection for this research was funded by the German Federal Ministry of Transport and Digital Infrastructure (Grant No. 16SBS001B ). ; Electric vehicles as part of corporate fleets play a key role in reducing CO2 emissions. However, business practice has shown that employees often refrain from using these newly procured fleet vehicles. The aim of this paper is therefore to explain the drivers and barriers of acceptance of electric vehicles on the employee's level. Based on a Grounded Theory approach, the authors extracted five main determinants from interviews with 16 experts. They were cross-checked with the literature including technology acceptance literature and electric vehicle research. To understand the dynamics of acceptance, panel data was collected in a longitudinal study with three waves in Germany. The panel data was analyzed using partial least squares (PLS) path modelling. Results show that employees' environmental concern as an attitudinal factor is the only short-term determinant triggering the first electric vehicle usage. Later, the influence of environmental concern disappears, while the impact of product-related determinants, such as enjoyment, ease of use, perceived risks, and relative advantage increases. Furthermore, a mediating effect of enjoyment emerges between ease of use on the intention to use an electric vehicle. Several important implications for decision-makers in management and policy are deduced to support short- and long-term usage of electric vehicles in corporate fleets. ; publishersversion ...

PurposeVendors' social cues – physical or behavioural hints – have an impact on the professional buyer. However, little is known about that impact. The purpose of this paper is to place knowledge about the impact of social cues that other disciplines acquired in the context of business-to-business (B2B) marketing to contribute constructively to the research agenda.Design/methodology/approachBy integrating findings on the processing of social cues and the behavioural response from the disciplines of neuroscience, biology and psychology (specifically the behavioural inhibition system [BIS]/behavioural activation system [BAS]-theory), this paper aims to provide an interdisciplinary perspective on the automatic evaluation of vendors by professional buyers.FindingsSocial cues are likely to be of substantial value in the (first) encounter between buyer and seller. Positively evaluated social cues create an approach-motivated behavioural intention, whereas negatively evaluated ones create avoidance. This process is probably predominantly mediated by trust and moderated by personality and contextual factors.Research limitations/implicationsThis paper stimulates research about the impact of social cues in a B2B context. While such knowledge would add practical value, this paper also explores possibilities for managers to use neuroscientific techniques to assess and train sales agents.Originality/valueThe impact of social cues is hardly covered in the B2B marketing literature, but they have an important impact on B2B decision-making. The conceptual framework combines the BIS/BAS theory (approach/avoidance) with the SOR-model (stimulus-organism-response), which is unique to the B2B marketing field.

Purpose
The purpose of this paper is to investigate selling actors in multi-actor sales ecosystems. When selling actors start taking over tasks that were formerly performed by salespeople, the distribution of tasks, allocation of responsibilities and finally the role of the salespeople changes. However, little is known about salespersons' perceptions of selling actors' identities and participation behavior in multi-actor sales ecosystems.


Design/methodology/approach
The authors conducted a World Café, a new qualitative method to the field of sales research, to obtain first data on selling actor identities in multi-actor sales ecosystems. Salespeople, who had the chance to observe and interact with more than 98,000 selling actors, disclosed their perceptions of selling actors' participation behavior in a multi-actor sales ecosystem. Four different data sources were analyzed using qualitative content analysis to develop a comprehensive understanding of the topic and to test validity through the convergence of information from different sources.


Findings
Using identity theory, a salesperson–selling actor relationship/behavior typology for multi-actor sales ecosystems was developed. Eight different selling actor identities were identified: avoider, observer, receptive actor, prepper, expecter, savvy actor, challenger and coworker.


Originality/value
The typology provides researchers and managers with a tool to better understand and evaluate sales ecosystems. This knowledge can be used as a starting point for the reassessment of the knowledge, skills and abilities necessary for salespeople in multi-actor sales ecosystems and to improve their training and coaching. The firsthand experiences reported by the participants of the World Café enable salespeople to identify different selling actors faster and prepare fitting approaches for all selling actor identities.

van Rijn, M., Kristal, S., & Henseler, J. (2019). Why do all good things come to an end? An inquiry into the discontinuation of sport sponsor–sponsee relationships. International Journal of Sports Marketing and Sponsorship, 20(2), 224-241. https://doi.org/10.1108/IJSMS-01-2018-0010 ; Purpose: The purpose of this paper is to explore the reasons for the discontinuation of sports sponsor–sponsee relationships and categorize them. Despite the negative outcomes of a sponsorship dissolution, research on this topic is rather scarce. Design/methodology/approach: The paper relies on an analysis of 24 historical cases and 19 in-depth interviews focusing on the Dutch soccer league. Several sponsorship disruptors are identified and clustered into four categories. Findings: The four categories for sponsorship dissolution are the following: sponsor-related factors, sponsee-related factors, inter-relational factors and external factors. In total, ten sponsorship disruptors are identified: insufficient value creation, objectives achieved, sports results, signal to society, exclusivity, negativity, personal relationship, changed marketing strategy, financial situation and legislation and regulation. Research limitations/implications: This study primarily investigates soccer sponsorship cases. Future research could investigate other sponsorship areas, which could yield different reasons for sponsorship termination. Practical implications: Practitioners are advised to view the sponsorship relationship as a strategic alliance, rather than a resource, from the beginning of the sponsorship. A solid relational framework is needed, which is built around the elements of trust, commitment and collaborative communication. If such a foundation does not exist or has eroded, the sponsorship relationship is fragile and can be endangered by various factors. Originality/value: This study uses inductive reasoning to devise a framework that enables sponsees to anticipate when sponsors are likely to discontinue their sponsorship such that the sponsees can take actions accordingly. Apart from validating existing reasons for sponsorship dissolution, this research also presents novel and previously undiscovered sponsorship disruptors. ; publishersversion ; published

Klesel, M., Schuberth, F., Henseler, J., & Niehaves, B. (2019). A test for multigroup comparison using partial least squares path modeling. Internet Research, 29(3), 464-477. https://doi.org/10.1108/IntR-11-2017-0418 ; Purpose: People seem to function according to different models, which implies that in business and social sciences, heterogeneity is a rule rather than an exception. Researchers can investigate such heterogeneity through multigroup analysis (MGA). In the context of partial least squares path modeling (PLS-PM), MGA is currently applied to perform multiple comparisons of parameters across groups. However, this approach has significant drawbacks: first, the whole model is not considered when comparing groups, and second, the family-wise error rate is higher than the predefined significance level when the groups are indeed homogenous, leading to incorrect conclusions. Against this background, the purpose of this paper is to present and validate new MGA tests, which are applicable in the context of PLS-PM, and to compare their efficacy to existing approaches. Design/methodology/approach: The authors propose two tests that adopt the squared Euclidean distance and the geodesic distance to compare the model-implied indicator correlation matrix across groups. The authors employ permutation to obtain the corresponding reference distribution to draw statistical inference about group differences. A Monte Carlo simulation provides insights into the sensitivity and specificity of both permutation tests and their performance, in comparison to existing approaches. Findings: Both proposed tests provide a considerable degree of statistical power. However, the test based on the geodesic distance outperforms the test based on the squared Euclidean distance in this regard. Moreover, both proposed tests lead to rejection rates close to the predefined significance level in the case of no group differences. Hence, our proposed tests are more reliable than an uncontrolled repeated comparison approach. Research limitations/implications: Current guidelines on MGA in the context of PLS-PM should be extended by applying the proposed tests in an early phase of the analysis. Beyond our initial insights, more research is required to assess the performance of the proposed tests in different situations. Originality/value: This paper contributes to the existing PLS-PM literature by proposing two new tests to assess multigroup differences. For the first time, this allows researchers to statistically compare a whole model across groups by applying a single statistical test. ; publishersversion ; published

AbstractOutliers can seriously distort the results of statistical analyses and thus threaten the validity of structural equation models. As a remedy, this article introduces a robust variant of Partial Least Squares Path Modeling (PLS) and consistent Partial Least Squares (PLSc) called robust PLS and robust PLSc, respectively, which are robust against distortion caused by outliers. Consequently, robust PLS/PLSc allows to estimate structural models containing constructs modeled as composites and common factors even if empirical data are contaminated by outliers. A Monte Carlo simulation with various population models, sample sizes, and extents of outliers shows that robust PLS/PLSc can deal with outlier shares of up to$$50\%$$50%without distorting the estimates. The simulation also shows that robust PLS/PLSc should always be preferred over its traditional counterparts if the data contain outliers. To demonstrate the relevance for empirical research, robust PLSc is applied to two empirical examples drawn from the extant literature.