Pathology
Ali Parvin; Behzad Souki; Tohfeh GHobadi Lamooki; Kambiz Hamidi
Abstract
Objective: Purpose: Political appointments in Iran’s public sector produce not only reduced organizational performance but also a systematic erosion of human capital. Existing, variable-oriented literature, however, lacks a human-centred, processual account of how such organizational events translate ...
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Objective: Purpose: Political appointments in Iran’s public sector produce not only reduced organizational performance but also a systematic erosion of human capital. Existing, variable-oriented literature, however, lacks a human-centred, processual account of how such organizational events translate into a multidimensional, individual-level harm process. This study fills that gap by designing and empirically validating a processual psychosocial–biological pathology model.Design/methodology/approach: The research employs a two-stage, exploratory-interpretive narrative qualitative design. In the first stage (exploration and model generation), narrative data from 13 in-depth interviews with employees were analysed using reflective thematic analysis to develop a conceptually grounded model based on participants’ experiences, meanings and sense-making. In the second stage (validation and refinement), the emergent model was pragmatically assessed for content validity and practical applicability through a Delphi procedure to achieve consensus among 10 executive experts.Findings: The thematic analysis produced an initial narrative of themes, which the Delphi rounds subsequently refined and integrated into a final 12-component model. The resulting model indicates that the pathology typically begins with perceptions of injustice and emotional, attitudinal and motivational reactions (psychological dimension); spreads to the erosion of trust, shared norms and organizational values and of social networks (social dimension); and culminates in the embodiment of stress and burnout (biological dimension).Originality/value: By adopting a human-centred narrative approach while preserving interpretive depth, this study offers a novel, empirically-anchored explanatory framework and an expert-validated model of employee pathology arising from political appointments. The model provides a scientific basis for designing preventive interventions aimed at preserving human capital.
Seyed Mohammadreza Torabipour; Reza Taghvaei; Kambiz Hamidi
Abstract
The separation of efficient and experienced employees may lead to substantial losses for an organization. Regarding direct and indirect costs of employee separation and the importance of employee role on an organization's performance, this research proposes a novel Employee Separation Management (ESM) ...
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The separation of efficient and experienced employees may lead to substantial losses for an organization. Regarding direct and indirect costs of employee separation and the importance of employee role on an organization's performance, this research proposes a novel Employee Separation Management (ESM) model with a focus on public organizations by using a mixed approach. Therefore, in this practical-exploratory research, data were collected through interviews and questionnaires. In the qualitative phase, 18 semi-structured interview rounds have been carried out with experts and managers of presidential administration by using the Snowball sampling method and the conceptual framework of the ESM model has been developed based on a thematic analysis. In the quantitative phase, after determining the statistical sample size by using the "Hair" method, 200 questionnaires were distributed among the statistical population and the proposed model has been validated based on the Structural Equation Modeling using the partial least squares approach. Environmental conditions, strategies, organizational, and contextual factors as well as behavioral aspects and human resource functions are found to be the key dimensions of the ESM model that may have individual, organizational, and social impacts. With a GOF value of 0.772, the Structural Equation Modeling indicates validity and strong modeling fit for the outcomes of the proposed model.
