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Digital-only fashion represents an ideal fusion of sustainability and fashionability, garnering growing interest among fashion professionals. However, there is a noticeable gap in research focusing on digital-only fashion acceptance among consumers. Hence, this study aims to empirically examine consumers’ motivations, evaluations and acceptance of digital-only fashion based on the Functional Theory of Attitudes.

A US-based research agency was hired to collect data, resulting in 247 completed survey responses. Data analysis was conducted using the Partial Least Squares Structural Equation Modeling (PLS-SEM) approach.

Testing results highlight that consumer acceptance of digital-only fashion is directly influenced by both overall attitude and self-expressive attitude. Self-expression is particularly pivotal in digital-only fashion acceptance. Adorning avatars and dressing realistic on-screen bodies are distinct yet complementary aspects of using digital-only fashion. Consumers with positive environmental beliefs about digital-only fashion are concerned about how well digital-only fashion items allow them to express such beliefs.

This study innovatively applied the functional theory of attitudes to the emerging domain of digital-only fashion and identified consumers’ four functional attitudes toward digital-only fashion, along with the underlying motivations served by each functional attitude. Furthermore, this study provides valuable practical insights across the digital-only fashion value chain.

The article explores which configurations of organizational and individual conditions support the purposeful use of performance information in the public sector. Prior research has predominantly focused on the effects of individual factors without paying as much attention to how these factors interact to influence public managers’ attitudes to integrating performance information into their decision-making.

The study employs a fuzzy-set qualitative comparative analysis (fs-QCA) to examine the different combinations of organizational and individual drivers that facilitate purposeful performance information use.

Goal clarity is a necessary but insufficient condition for purposeful information use. It needs to be complemented by a mature performance management system, public managers with prosocial motivation who engage in extra-role behaviours within a non-innovative organizational culture, or a developmental culture that motivates managers who are unaware of the social impact generated by their work.

The case selection does not allow for direct generalizations. Future studies could replicate the configurational analysis in different countries and sectors and introduce additional environmental, organizational, and individual conditions.

The study suggests the need to integrate actions that support the purposeful use of performance information and define clear departmental goals. Although the latter is a necessary condition, it needs to be supported by other organizational and individual factors.

The study deepens the theory of the drivers of purposeful performance information use in the public sector by adopting a configurational approach and exploring how organizational and individual conditions interact to foster information use.

L. Gǎvruţa (2012) introduced a special kind of frames, named K-frames, where K is an operator, in Hilbert spaces, which is significant in frame theory and has many applications. In this paper, first of all, we have introduced the notion of approximative K-atomic decomposition in Banach spaces. We gave two characterizations regarding the existence of approximative K-atomic decompositions in Banach spaces. Also some results on the existence of approximative K-atomic decompositions are obtained. We discuss several methods to construct approximative K-atomic decomposition for Banach Spaces. Further, approximative _d-frame and approximative _d-Bessel sequence are introduced and studied. Two necessary conditions are given under which an approximative _d-Bessel sequence and approximative _d-frame give rise to a bounded operator with respect to which there is an approximative K-atomic decomposition. Example and counter example are provided to support our concept. Finally, a possible application is given.

This study aims to focus on the optimal green R&D of a capital-constrained supply chain under different channel power structures as well as the impact of capital constraint, financing cost, channel power structure and cost-reducing efficiency on green R&D and supply chain profitability.

A two-echelon supply chain is considered. The upstream firm engages in green R&D but has capital constraints that can be overcome by external financing. Green R&D is beneficial to reduce production costs and increase consumer demand. Based on whether or not the upstream firm is capital constrained and dominates the supply chain, four models are developed.

Capital constraints significantly lower green R&D and supply chain profitability. Transferring leadership from the upstream to the downstream firms leads to higher green R&D levels and downstream firm profitability, whereas the upstream firm's profitability is increased (decreased) if green R&D investment efficiency is high (low) enough. Greater financing costs reduce green R&D and downstream firm profitability; however, the upstream firm's profitability under the model in which it functions as the follower increases if the initial capital is sufficient. More importantly, empirical analysis based on practice data is used to verify the theoretical results reported above.

This study reveals how upstream firms in supply chains decide green R&D decisions in situations with capital constraints, providing managers and governments with an understanding of the impact of capital constraint, channel power structure, financing cost and cost-reducing efficiency on supply chain green R&D and profitability.

The major contributions are the exploration of supply chain green R&D by taking into consideration channel power structures and cost-reducing efficiency and the validation of theoretical results using practice data.