{"id":112774,"date":"2026-02-05T10:04:33","date_gmt":"2026-02-05T09:04:33","guid":{"rendered":"https:\/\/industry-science.com\/?post_type=article&p=112774"},"modified":"2026-03-06T13:57:10","modified_gmt":"2026-03-06T12:57:10","slug":"jocat-job-change-acceptance-toolbox","status":"publish","type":"article","link":"https:\/\/industry-science.com\/en\/articles\/jocat-job-change-acceptance-toolbox\/","title":{"rendered":"JOCAT (Job Change Acceptance Toolbox)"},"content":{"rendered":"\n

Artificial Intelligence (AI) is transforming work in unprecedented ways. As an umbrella term for technologies emulating human cognition, AI automates routine tasks and augments decision-making, reasoning, and content generation [1]. Characterized by autonomy, self-learning, and opacity, AI systems<\/a> differ fundamentally from previous technologies, enabling deeper and broader transformations of organizational structures, processes, and cultural dynamics [2, 3]. As a result, AI implementation generates heightened uncertainty and resistance, frequently rooted in ethical and psychological concerns [3, 4].<\/p>\n\n\n\n

While these challenges are intensified by AI\u2019s complexity, many observed reactions reflect familiar patterns of organizational change. Hence, revisiting foundational change models can help illuminate conditions under which transformation succeeds. Over the past decades, numerous frameworks have emerged (e.g., by Lewin, K\u00fcbler-Ross, Leavitt, Prosci, Bridges, and Kotter), each emphasizing distinct but important aspects of change (see Fig. 1<\/strong>). Broader concepts such as Organizational Development (OD) and Action Research (AR) complement these models by highlighting iterative learning, stakeholder engagement, and cultural alignment, elements which are all also essential for sustainable transformation [5, 6].<\/p>\n\n\n\n

\"Figure
Figure 1: Overview of influential change management models.<\/em><\/figcaption><\/figure>\n\n\n\n

A central insight shared across these models is that successful change depends on people and their willingness to engage [13, 14]. Nevertheless, most models remain fragmented, focusing on isolated aspects rather than offering an integrated perspective [15, 16]. For instance, participation and involvement are stressed in ADKAR [10] and OID [9] models, while communication is central to Kotter\u2019s model [12] and K\u00fcbler-Ross\u2019 emotional curve [8]. Leadership is treated as a key lever in Kotter\u2019s model [12], whereas OD approaches [6] emphasize cultural alignment as both a change driver and barrier. <\/p>\n\n\n\n

Considering this fragmentation, recent reviews [13-17] emphasize the need for integrated, adaptable frameworks that address these factors jointly. These should enable more holistic as well as ethically and psychologically responsible approaches to contemporary transformation processes.<\/p>\n\n\n\n

However, rooted in earlier technological paradigms, most models, even in integrated form, fall short of addressing the volatile, adaptive, and data-intensive nature of AI systems [3]. To adequately respond to today\u2019s transformation demands, change frameworks must also account for the dynamic interplay between humans and AI, the opacity of algorithmic processes, and ethical challenges tied to data use and augmentation [3, 18]. This leads to the guiding question of this article: How can change management approaches be designed to meet the unique psychological, ethical, and structural demands of AI system implementation?<\/p>\n\n\n\n

To address this question, a two-part mixed-method approach is adopted. The first part establishes a holistic view on change processes by integrating classical models and applying them to a use case from FraAlliance (a joint venture between Fraport AG and Lufthansa). The use case reveals the conceptual gaps present when it comes to meeting AI-specific demands, which are further substantiated by insights from literature on AI-related transformation processes. The second part draws on six expert interviews to explore the impact that these gaps have in practice. This comes together to inform the development of the Job Change Acceptance Toolbox (JOCAT), a human-centered, practice-oriented framework for AI-related change.<\/p>\n\n\n\n

Part I: Integration of change models and identification of AI-specific gaps<\/h2>\n\n\n\n

This first part aims to develop an integrated framework offering a holistic view on change processes, which is essential for sustainable transformation. The framework is derived by integrating the established change models summarized in Figure 1 <\/strong>and grounded in findings from recent literature reviews [see 13-17, 19, 20]. This integrated model is subsequently applied to a real change case (FraAlliance) involving AI-based baggage detection and further enriched with insights from literature on AI-related change processes [see 21-25] to uncover gaps in addressing AI-specific change requirements.<\/p>\n\n\n\n

Deriving an integrated change management model from literature<\/h3>\n\n\n\n

A suitable starting point for an integrated change management framework from an organizational psychology perspective remains Lewin\u2019s [7] three-step model, which offers enduring processual clarity and has shaped many subsequent approaches [13, 14]. However, sustainable transformation demands more than processual clarity; it requires a focus on content-related drivers and interdependent success factors that enable effective change [16]. To that end, seven cross-cutting factors occur across the models in Figure 2<\/strong>, including communication and participation<\/em>, leadership<\/em>, processes<\/em>, strategy and innovation<\/em>, training and development<\/em>, culture and accountability<\/em>, and people and teams<\/em> [14, 20]. <\/p>\n\n\n\n

In line with the OID model [9], these factors must be understood as interrelated system elements that both influence and are influenced by change. While all seven factors are considered relevant across all phases, their influence varies by phase, with certain factors gaining salience at specific stages without excluding others:<\/p>\n\n\n\n