Partial Least Squares Structural Equation Modeling: Rigorous Applications, Better Results and Higher Acceptance

Information and communications technologies ICTs have enabled the rise of so-called "Collaborative Consumption" CC: the peer-to-peer-based activity of obtaining, giving, or sharing the access to goods and services, coordinated through community-based online services. CC has been expected to alleviate societal problems such as hyper-consumption, pollution, and poverty by lowering the cost of economic coordination within communities. However, beyond anecdotal evidence, there is a dearth of understanding why people participate in CC. Therefore, in this article we investigate people's motivations to participate in CC. The study employs survey data N=168 gathered from people registered onto a CC site. The results show that participation in CC is motivated by many factors such as its sustainability, enjoyment of the activity as well as economic gains. An interesting detail in the result is that sustainability is not directly associated with participation unless it is at the same time also associated with positive attitudes towards CC. This suggests that sustainability might only be an important factor for those people for whom ecological consumption is important. Furthermore, the results suggest that in CC an attitude-behavior gap might exist; people perceive the activity positively and say good things about it, but this good attitude does not necessary translate into action.

The sharing economy: Why people participate in collaborative consumption

Partial least squares structural equation modeling (PLS-SEM) has become a key multivariate analysis technique that human resource management (HRM) researchers frequently use. While most disciplines...

Partial least squares structural equation modeling in HRM research

Based on a sample of 169 subsidiaries of multinational corporations (MNCs) operating in the USA, Russia, and Finland, this paper investigates the relationship between MNC subsidiary HRM practices, absorptive capacity and knowledge transfer. First, we examine the relationship between the application of specific HRM practices and the level of the absorptive capacity. Second, we suggest that absorptive capacity should be conceptualized as being comprised of both employees’ ability and motivation. Further, results indicate that both ability and motivation (absorptive capacity) are needed to facilitate the transfer of knowledge from other parts of the MNC.

/pdf/mnc-knowledge-transfer-subsidiary-absorptive-capacity-and-59b9kn88a7.pdf

MNC knowledge transfer, subsidiary absorptive capacity, and HRM

The stereotype content model (SCM) proposes potentially universal principles of societal stereotypes and their relation to social structure. Here, the SCM reveals theoretically grounded, cross-cultural, cross-groups similarities and one difference across 10 non-US nations. Seven European (individualist) and three East Asian (collectivist) nations (N=1,028) support three hypothesized cross-cultural similarities: (a) perceived warmth and competence reliably differentiate societal group stereotypes; (b) many out-groups receive ambivalent stereotypes (high on one dimension; low on the other); and (c) high status groups stereotypically are competent, whereas competitive groups stereotypically lack warmth. Data uncover one consequential cross-cultural difference: (d) the more collectivist cultures do not locate reference groups (in-groups and societal prototype groups) in the most positive cluster (high-competence/high-warmth), unlike individualist cultures. This demonstrates out-group derogation without obvious reference-group favouritism. The SCM can serve as a pancultural tool for predicting group stereotypes from structural relations with other groups in society, and comparing across societies.

/pdf/stereotype-content-model-across-cultures-towards-universal-235gnzvh4f.pdf

Stereotype content model across cultures: towards universal similarities and some differences.

Regression: A Second Course in Statistics@@@Multivariate Data Analysis

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Regression: A Second Course in Statistics@@@Multivariate Data Analysis