This research proposal explores German hiring managers’ intentions to adopt artificial intelligence (AI) in recruitment and selection. Based on the Technology Acceptance Model (TAM), the proposed study will investigate how perceived usefulness and ease of use influence adoption intentions, while also examining the potential impact of trust, perceived risks, and organizational innovation culture. Using a quantitative survey and hierarchical regression analysis, the study aims to identify the key factors that may shape the acceptance of AI in German organizations. The findings are intended to contribute to research on technology acceptance and offer practical insights into the responsible, transparent, and effective implementation of AI in human resource management.
Table of Contents
1. Introduction
1.1 Research Objective
1.2 Target Audience
1.3 Scope and Delimitations
2. Literature Review
2.1 Conceptual Assumptions
2.1.1 Artificial Intelligence (AI)
2.1.2 Perceived Usefulness
2.1.3 Perceived Ease of Use
2.1.4 Trust and Perceived Risks
2.1.5 Intention to Adopt
2.1.6 Organizational Innovation Culture
2.2 Theoretical Works
2.2.1 Technology Acceptance Models
2.2.2. Trust and Perceived Risks
2.2.3 Organizational Context of Innovation
2.3 Selected Empirical Studies
2.4 Research Gap
3. Research Design
3.1 Assumptions and Methodological Approach
3.2 Methods and Instruments
3.3 Population and Sampling
3.4 Data Analysis and Synthesis
3.5 Validity and Reliability
3.6 Methodological Constraints
3.7 Ethical Considerations
4. Expected Outcomes
Objectives & Topics
This research proposal aims to investigate how hiring managers in German enterprises evaluate artificial intelligence tools in recruitment and selection processes, and how these perceptions determine their behavioral intention to adopt such technologies. Drawing upon Davis's Technology Acceptance Model (TAM), the study addresses the core factors of perceived usefulness and perceived ease of use, while extending the model by incorporating cognitive and affective trust, multidimensional perceived risks, and perceived organizational innovation culture within the stringent regulatory environment of Germany.
- Evaluation of perceived usefulness and ease of use in AI-assisted candidate pre-screening and selection.
- Investigation of trust dimensions (cognitive and affective) and perceived risks under GDPR and EU AI Act regulations.
- Analysis of organizational innovation culture as an environmental driver or barrier for technological change.
- Quantitative survey methodology specifically tailored to German line managers and recruitment decision-makers.
- Hierarchical regression modeling to test incremental predictive power beyond traditional technology acceptance variables.
Excerpt from the Book
1. Introduction
In Human Resource Management (HRM) recruitment and selection are inseparable core functions (Miebach, 2017, p. 83), commonly referred to as the hiring process. For companies, hiring the right people is essential, as bad hiring decisions can lead to high costs, increased turnover, and low performance (Olson et al., 2018).
Digital technologies like artificial intelligence (AI), specifically machine learning (ML) and generative artificial intelligence (GenAI), are reshaping the hiring process. Organizations use ML tools in the hiring process, for instance, to pre-screen applicants, predict job performance, and match and rank candidates against job profiles. GenAI tools are used, for instance, to generate standardized interview guides and questions, or to support employee selection decisions.
From an HRM perspective, AI solutions can be strategic resources that contribute to competitive advantages. AI solutions also offer operational benefits such as greater efficiency, scalability, and consistency in HR workflows (Chowdhury et al., 2023). At the same time, AI tools in HR face specific challenges. AI based decisions about people intersect with long-standing professional and ethical standards. For hiring managers, this shift may also be perceived as a challenge to their professional expertise or decision autonomy. In addition, both the legal system and prevailing societal norms place constraints on the use of AI in HR (Tambe, Cappelli & Yakubovich, 2019, p. 16). However, when designed and applied in transparent and fair ways, AI systems can help reduce certain forms of human bias in decision-making. Yet despite these potential benefits, people may resist such technologies when they are perceived as unclear, intrusive, or dehumanizing (Zirar, Ludwig & Pikos, 2022).
In the German context, these technological developments unfold under specific structural and regulatory conditions. Companies face demographic change, a lack of skilled workers and growing regulatory demands. Especially the General Data Protection Regulation (GDPR) and the European Artificial Intelligence Act (EU AI Act) increase legal uncertainty and raise compliance requirements.
Chapter Overview
1. Introduction: Outlines the background of AI adoption within recruitment and selection, defines the research questions and core hypotheses based on an extended TAM, and specifies the target audience as well as the study scope.
2. Literature Review: Synthesizes conceptual definitions and theoretical models surrounding technology acceptance, trust, perceived risk, and innovation culture, while analyzing recent empirical evidence and establishing the research gap concerning German hiring managers.
3. Research Design: Formulates the critical realist and quantitative methodology, describing the standardized survey instrument, sampling framework of 150–160 German hiring professionals, hierarchical regression procedures, validity and reliability protocols, and research ethics.
4. Expected Outcomes: Discusses hypothesized relationships between technology beliefs, uncertainty dimensions, and adoption intentions, concluding with theoretical contributions to HR literature as well as practical, policy, and societal implications.
Keywords
Artificial Intelligence, Recruitment and Selection, Technology Acceptance Model, Hiring Managers, Perceived Usefulness, Perceived Ease of Use, Trust, Perceived Risk, Organizational Innovation Culture, Human Resource Management, Germany, GDPR, EU AI Act, Algorithmic Decision-Making, Hierarchical Regression.
Frequently Asked Questions
What is the central focus of this research proposal?
The research examines the determinants that influence German hiring managers' intentions to adopt artificial intelligence systems during candidate recruitment and selection workflows, utilizing an extended technology acceptance framework.
What are the primary theoretical themes covered?
The study integrates classical technology acceptance theory (TAM) with interpersonal and organizational trust concepts, multi-faceted perceived risk assessments, and the moderating role of organizational innovation culture.
What is the main objective and core research question?
The primary objective is to evaluate whether adding trust, perceived risk, and organizational innovation culture provides incremental explanatory power beyond perceived usefulness and ease of use in predicting managers' adoption of AI hiring tools.
Which empirical methodology is proposed?
A cross-sectional quantitative design is utilized, relying on a standardized online survey administered to 150–160 corporate hiring managers in Germany, evaluated via exploratory factor analysis and hierarchical multiple regression.
What core aspects are examined in the main body?
The main body investigates conceptual definitions of AI in HR, evaluates prior empirical findings on algorithmic bias and recruiter attitudes, identifies existing literature gaps, and specifies operational measurement scales for all proposed constructs.
Which keywords characterize the proposal?
Key terms characterizing the study include Artificial Intelligence, Hiring Managers, Technology Acceptance Model (TAM), Cognitive and Affective Trust, Perceived Risk, Organizational Innovation Culture, and Recruitment and Selection.
Why is the German organizational and legal context emphasized in this study?
Germany possesses a distinct institutional landscape characterized by strict regulatory frameworks—notably the GDPR and the EU AI Act—alongside high societal skepticism toward algorithmic decisions, making legal compliance and perceived risk pivotal factors in technology adoption.
How does the author differentiate the concept of trust regarding AI systems?
Building on McAllister's framework, trust is operationalized across two dimensions: cognitive trust, which relates to rational assessments of system accuracy, reliability, and competence; and affective trust, which reflects emotional confidence and comfort regarding algorithmic involvement in high-stakes people decisions.
What role does organizational innovation culture play in the proposed model?
Perceived organizational innovation culture is introduced as a contextual variable that encourages experimentation, learning from failure, and risk tolerance, thereby potentially moderating risk perceptions and fostering positive intentions toward innovative AI adoption.
- Quote paper
- Christina Schuster (Author), 2026, Exploring German Hiring Managers' Intentions to Adopt AI. An Extended Technology Acceptance Model Approach, Munich, GRIN Verlag, https://www.grin.com/document/1772113
