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The purpose of this article is twofold. Its first objective is to bring to the fore the unexplored and neglected origins of social exchange theory (SET) to critique this body of conjecture. This unearthing is illustrated through focusing on the way the theory was developed and how this development was mischaracterised in literature. Its second objective is to invoke the methodological assemblage of ANTi-History and the “close reading” notion using multiple archival sources to demonstrate their usefulness within the critical qualitative method debate.

The historic character of management and organization studies is exemplified through utilizing a combination of textual sources to examine how SET emerged from within the human relations school of thought throughout much of the twentieth century. Specifically, an array of sources (including archival data) is deployed and closely examined to trace how SET formed and became prevalent in organizational studies over the last decades.

SET is not only indebted to the human relations movement in general and to Elton Mayo’s work in particular (as is well-known), but also to logical positivism and behavioralist-school psychology. As such, Homans’ work marked the beginning of a new era in organizational behavior research.

The article highlights the role of historical analyses in interpreting mainstream constructs in organizational behavior. In doing so, it reveals how critical qualitative research leads to understanding some shortcomings of a theory and indicates potential remedies.

This paper aims to examine the health technology use in health information seeking, communication and personal health information management, as well as in the effects they may have on his relationship with the physician and on the consumption of medical resources.

An online survey was conducted. The questionnaires were distributed via online health discussion forums using Google's survey software with a summary presentation of the study’s objective. The final selection of 362 individuals was made using social media, direct email and collaboration with community groups. The empirical validation of the causal model was conducted using the partial least square approach.

The results show that the use of e-health strengthens the quality of the patient–physician relationship and patient empowerment while increasing the consumption of medical resources.

The results of this research indicate that the internet has transformed the relationship of patients to health, to their doctors and to the health-care system. In this new context, a reconsideration of the status of the patient must be considered by health service providers.

In the paradox of personalized services and privacy risks, what factors influence users’ decisions is considered an interesting issue worth exploring. The current study aims to empirically explore privacy behavior of social media users by developing a theoretical model based on privacy calculus theory.

Privacy risks, conceptualized as natural risks and integrated risks, were proposed to affect the intention of privacy disclosure and protection. The model was validated through a hybrid approach of structural equation modeling (SEM)-artificial neural network (ANN) to analyze the data collected from 527 effective responses.

The results from the SEM analysis indicated that social interaction and perceived enjoyment were strong determinants of perceived benefits, which in turn played a dominant role in the intention to disclose the privacy in social media. Similarly, trust and privacy invasion experience were significantly related to perceived risks that had the most considerable effect on users’ privacy protection intention. And the following ANN models revealed consistent relationships and rankings with the SEM results.

This study broadened the application perspective of privacy calculus theory to identify both linear and non-linear effects of privacy risks and privacy benefits on users’ intention to disclose or protect their privacy by using a state-of-the-art methodological approach combining SEM and ANN.