A Conceptual Model for Human-Centric AI Adoption in Manufact…

Topic:

A Conceptual Model for Human-Centric AI Adoption in Manufacturing Projects: Integrating STS and TAM

• Focus: Develop and present the novel integrated socio-technical model that bridges STS (Socio-technical systems) theory and technology acceptance models (TAM/UTAUT) in the context of manufacturing project management.
• Journal Type: Project Management Journal, International Journal of Project Management, Technological Forecasting & Social Change
• Key Contributions: Theoretical integration, conceptual framework validation, and propositions for future research.

Detail: “A Conceptual Model for Human-Centric AI Adoption in Manufacturing Projects[1]: Integrating STS and TAM”

This paper will be a conceptual/theoretical piece that lays the foundation for the dissertation. It will propose a novel model that integrates Socio-Technical Systems (STS) theory and the Technology Acceptance Model (TAM) to explain the adoption of human-centric AI in manufacturing projects.

Key elements of the paper:

1. Introduction:

• Set the context: digital transformation in manufacturing, the rise of AI, and the specific challenges of AI adoption in project management.
• Highlight the gap: current literature lacks a unified socio-technical perspective that considers both the social (human, organizational) and technical (AI technology) factors in the context of manufacturing projects.
• State the purpose: to propose a conceptual model that integrates STS and TAM to explain and predict the adoption of human-centric AI in manufacturing projects.

1. Theoretical Background:

• Socio-Technical Systems (STS) Theory: Explain the key principles of STS, focusing on the interaction between social subsystems (people, structure, culture) and technical subsystems (technology, tools, processes) and how they jointly determine system performance.
• Technology Acceptance Model (TAM) and UTAUT: Review the core constructs of TAM/UTAUT (perceived usefulness, perceived ease of use, social influence, facilitating conditions, etc.) and their application in technology adoption research.
• Human-Centered AI (HCAI): Discuss the principles of HCAI (explainability, transparency, accountability, human augmentation) and why they are critical for AI adoption in project management.
• Project Management Context: Briefly discuss the unique characteristics of manufacturing projects (temporary, unique, constrained) and the specific knowledge areas (risk, schedule, cost) where AI can be applied.

1. Development of the Conceptual Model:

• Propose a model that integrates STS and TAM in the context of HCAI adoption in manufacturing projects.
• The model should include:
• Independent Variables (Socio-Technical Factors):
• Social Subsystem: organizational readiness, team culture, leadership support, skills, and training.
• Technical Subsystem: AI tool characteristics (e.g., transparency, explainability, compatibility, complexity), data infrastructure, and integration capabilities.
• Mediating Variables (TAM Constructs): perceived usefulness, perceived ease of use, trust in AI, and intention to use.
• Dependent Variables: adoption behavior (actual use) and project performance (efficiency, effectiveness, stakeholder satisfaction).
• The model should also consider moderating variables such as project complexity and organizational size.

1. Propositions:

• Based on the model, develop a set of testable propositions. For example:
• P1: The social subsystem factors (e.g., organizational readiness) will positively influence the perceived usefulness and ease of use of HCAI tools.
• P2: The technical subsystem factors (e.g., AI transparency) will positively influence trust in AI, which in turn will positively influence perceived usefulness and ease of use.
• P3: Perceived usefulness and ease of use will mediate the relationship between socio-technical factors and the intention to use HCAI tools.
• P4: The intention to use will lead to actual adoption, which will positively impact project performance.
• P5: Project complexity will moderate the relationship between adoption and project performance, such that the relationship is stronger for more complex projects.

1. Discussion:

• Discuss the theoretical contributions of the model, notably how it bridges STS and TAM in a novel context (HCAI in manufacturing projects).
• Highlight the practical implications for project managers and organizations seeking to adopt AI in their projects.
• Acknowledge the limitations of the conceptual model and the need for empirical validation.

1. Conclusion and Future Research:

• Summarize the model and its potential contributions.
• Suggest future research directions, including empirical testing of the model (which will be done in the subsequent phases of the dissertation).

This paper will be primarily theoretical and it sets the stage for the empirical work.

Detailed Proposal for Paper 1 (please note this is just a proposal i.e for information to get the context!!):

“A Conceptual Model for Human-Centric AI Adoption in Manufacturing Projects: Integrating STS and TAM”

Abstract (Proposed)

This paper proposes a novel conceptual model that integrates Socio-Technical Systems (STS) theory with the Technology Acceptance Model (TAM) to explain the adoption of Human-Centric AI (HCAI) in manufacturing project management. The model bridges macro-level organizational and project contexts with micro-level individual perceptions, addressing a critical gap in literature where technical and human factors are often studied in isolation. We argue that successful HCAI adoption requires simultaneous alignment of the social subsystem (team dynamics, skills, governance) with the technical subsystem (AI tools, data infrastructure), mediated by individual acceptance factors (perceived usefulness, trust). The paper contributes a unified theoretical framework for researchers and a structured assessment tool for practitioners aiming to implement AI in complex project environments.

1. Introduction & Motivation

• Context: Digital transformation in manufacturing; AI’s potential beyond automation to augment project management.
• Problem: Current AI adoption models are either too technical (focusing on algorithms) or too psychological (focusing on individual acceptance), neglecting the interactive socio-technical dynamics unique to temporary, unique manufacturing projects.
• Research Gap: Lack of an integrated model that connects STS macro-factors with TAM micro-mechanisms in the context of HCAI and project management.
• Objective: To develop and present a novel conceptual model that explains how socio-technical alignment drives HCAI adoption and subsequent project performance.

2. Theoretical Foundation & Model Development

2.1 Core Theories Integrated:

• Socio-Technical Systems (STS) Theory:
• Social Subsystem: Project team structure, skills, culture, leadership, PMO governance.
• Technical Subsystem: HCAI tools (explainable AI, dashboards), IT infrastructure, data quality, integration APIs.
• Principle: Optimal performance requires joint optimization of both subsystems.
• Technology Acceptance Model (TAM/UTAUT2):
• Core Constructs: Perceived Usefulness (PU), Perceived Ease of Use (PEOU), Social Influence, Facilitating Conditions.
• Extended with Trust in AI and Perceived Transparency (from HCAI literature).
• Human-Centered AI (HCAI) Principles:
• Explainability, transparency, accountability, human augmentation (not replacement).

2.2 Proposed Conceptual Model:

text

[Organizational & Project Context]

|

v

[SOCIO-TECHNICAL ALIGNMENT]

| |

v v

Social Subsystem Technical Subsystem

(Team readiness, (Tool compatibility,

culture, skills) data infrastructure)

| |

v v

[TAM MEDIATORS: PU, PEOU, Trust]

|

v

[HCAI ADOPTION INTENTION & BEHAVIOR]

|

v

[PROJECT PERFORMANCE OUTCOMES]

|

v

[Moderators: Project Complexity, Formal Integration]

2.3 Propositions/Hypotheses Derived from the Model:

1. P1: Higher socio-technical alignment (STS fit) positively influences perceived usefulness and ease of use of HCAI tools.
2. P2: Technical subsystem quality (e.g., tool transparency) directly enhances user trust in AI.
3. P3: Trust mediates the relationship between technical subsystem characteristics and adoption intention.
4. P4: Social subsystem readiness (e.g., team AI literacy) strengthens the relationship between perceived usefulness and adoption.
5. P5: The effect of HCAI adoption on project performance is moderated by project complexity and formal integration into PM processes.

3. Contribution to Theory & Practice

Theoretical Contributions:

• Integrates STS theory with technology adoption models in a novel context (manufacturing projects).
• Introduces HCAI principles as critical moderators in the acceptance process.
• Provides a multi-level framework (organizational, team, individual, tool) for analyzing AI adoption.

Practical Implications:

• Assessment Tool: Organizations can use the model to diagnose readiness for AI adoption.
• Implementation Roadmap: Identifies leverage points for intervention (e.g., improving transparency vs. upskilling teams).
• PMO Guidance: Helps PMOs design AI integration strategies that balance technical and human factors.

4. Methodological Note (for the Paper)

• The model is derived from systematic literature synthesis across STS, TAM, HCAI, and PM fields.
• It will be empirically tested in subsequent papers (validated via survey and case studies).
• The paper itself is conceptual but grounded in extant theory and emerging empirical evidence.

5. Proposed Structure of the Paper

1. Introduction
2. Literature Review: STS, TAM/UTAUT, HCAI, AI in PM
3. Gap Identification & Research Question
4. Conceptual Model Development
5. Proposition Formulation
6. Theoretical & Practical Implications
7. Limitations & Future Research
8. Conclusion

Next Steps for Developing This Paper:

1. Deep dive into STS-TAM integration literature
2. Refine the visual model using diagramming tools
3. Develop detailed propositions with clear theoretical justification.
4. Write the manuscript targeting 8,00010,000 words.

This paper would establish you as a scholar bridging socio-technical theory with AI adoption research in the project management domaina unique and timely contribution.

[1] Definition of Core Concept: Manufacturing Projects

For this research, “manufacturing projects” are defined as temporary, uniqueendeavors undertaken within a manufacturing environment that are managed asdistinct projects due to their complexity, uncertainty, and defined scope, time, andcost constraints. This includes:

New Product Introduction (NPI) / Launch Projects

Capital Project Execution (e.g., new production line installation)

Digital Transformation / Industry 4.0 Implementation Projects

Major Process Re-engineering Projects

This definition explicitly excludes the management of repetitive, steady-stateproduction operations.