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The study aimed to clarify differences in fabric hand perceptions among Japanese and Chinese participants and implement online shopping strategies that enable consumers to easily recognize fabric texture.

Forty (20 Japanese and 20 Chinese) participants knowledgeable about clothing and fabric were recruited. Participants evaluated fabric by sight and touch in a visuotactile experiment (VTE). The stimulus material comprised 39 fabric samples representing a broad range of fabric attributes (7 fibers, 5 weaving/knitting techniques and 3 yarn thicknesses and density). A Mann–Whitney U test and a factor analysis were conducted to determine differences in responses for the different fabric variables.

The fabric hand perceptions factors were similar between both groups. Japanese participants showed a stronger preference for fabrics that felt wet. Japanese participants’ fabric hand perceptions had a 3-factor structure, while Chinese participants had a 2-factor structure. Chinese participants regarded “crisp” as perceptually and linguistically equivalent to “stretchy.”

The study’s findings suggest that Chinese people have stronger preferences in fabrics than Japanese people do. Japanese people evaluate fabric hand in a more nuanced manner than Chinese individuals, including discerning different fabric attributes, such as fiber and yarn thickness and density. Thus, nationality may influence fabric hand perceptions more than fabric knowledge does. Specifically, in evaluating “crispness,” the results required further analysis because differences in nationality may have affected evaluations regarding perception and linguistic perspectives. The findings provide design guidelines for implementing online shopping strategies adapted to each participant group.

Existing literature on algorithmic management practices – defined as autonomous data-driven decision making in people's management by adoption of self-learning algorithms and artificial intelligence – suggests complex relationships with employees' well-being in the workplace. While the use of algorithms can have positive impacts on people-related decisions, they may also adversely influence job autonomy, perceived justice and – as a result – workplace well-being. Literature review revealed a significant gap in empirical research on the nature and direction of these relationships. Therefore the purpose of this paper is to analyse how algorithmic management practices directly influence workplace well-being, as well as investigating its relationships with job autonomy and total rewards practices.

Conceptual model of relationships between algorithmic management practices, job autonomy, total rewards and workplace well-being has been formulated on the basis of literature review. Proposed model has been empirically verified through confirmatory analysis by means of structural equation modelling (SEM CFA) on a sample of 21,869 European organisations, using data collected by Eurofound and Cedefop in 2019, with the focus of investigating the direct and indirect influence of algorithmic management practices on workplace well-being.

This research confirmed a moderate, direct impact of application of algorithmic management practices on workplace well-being. More importantly the authors found out that this approach has an indirect influence, through negative impact on job autonomy and total rewards practices. The authors observed significant variation in the level of influence depending on the size of the organisation, with the decreasing impacts of algorithmic management on well-being and job autonomy for larger entities.

While the influence of algorithmic management on various workplace practices and effects is now widely discussed, the empirical evidence – especially for traditional work contexts, not only gig economy – is highly limited. The study fills this gap and suggests that algorithmic management – understood as an automated decision-making vehicle – might not always lead to better, well-being focused, people management in organisations. Academic studies and practical applications need to account for possible negative consequences of algorithmic management for the workplace well-being, by better reflecting complex nature of relationships between these variables.