Antecedents of Consumer Acceptance of Technology Agency – Model

Abstract

The Antecedents of Consumer Acceptance Technology Agency Model (ACATAM), developed by Morosan and Dursun-Cengizci (2024), is a specialized 22-item instrument designed to investigate the factors that influence consumer willingness to delegate decision-making authority to technology, specifically artificial intelligence (AI) systems. The study focused empirically on hotel guests’ willingness to allow AI-based systems to make decisions on their behalf during their stays. The primary objective was to assess the crucial role of perceived ethics in determining consumer acceptance of technology agency within the hospitality sector.

The survey items were either adapted from established literature (e.g., Xu et al., 2011; Roman, 2007) or newly developed to capture key constructs, including perceived benefits, risks, loss of competence, unpredictability, and convenience orientation. To mitigate threats such as common method bias, the researchers employed methodological safeguards, including varying scale placement and reassuring respondents of the anonymity of their answers. Administered to a sample of U.S. hotel guests, the final measure demonstrated a robust factor structure, high reliability, and strong validity metrics.

Keywords

Acceptance of Technology Agency, Artificial Intelligence, Convenience Orientation, Hospitality Industry, Measurement Model, Perceived Benefits, Perceived Ethics, Perceived Loss of Competence, Perceived Risks, Perceived Unpredictability

Authors

Cristian Morosan, Aslıhan Dursun-Cengizci

Purpose

The central purpose of the ACATAM is to empirically assess the extent to which consumers, particularly hotel guests, are willing to accept the agency of technological systems, such as Artificial Intelligence (AI), to make decisions on their behalf. This assessment is framed around understanding the influence of various antecedents, with a particular emphasis on the consumer’s perception of the ethical implications of such delegation.

The scale serves as a diagnostic tool for researchers and industry professionals interested in the adoption barriers and facilitators related to increasingly autonomous technologies in service settings. By isolating factors like perceived risks and benefits, the instrument helps predict consumer behavior regarding technology integration and acceptance of decision-making authority.

Construct

The primary psychological construct measured is Consumer Technology Acceptance, specifically focusing on the advanced concept of “technology agency.” Technology agency refers to the consumer’s willingness to cede control or delegate decision-making responsibilities to an automated or intelligent system.

The ACATAM operationalizes this overarching construct through seven distinct factors, providing a nuanced view of the decision-making process. These factors move beyond traditional technology acceptance models by incorporating affective and ethical dimensions related to autonomy and control, such as Perceived Loss of Competence and Perceived Ethics.

Validity

The validity of the ACATAM was established through rigorous structural equation modeling techniques, specifically Confirmatory Factor Analysis (CFA). Both convergent and discriminant validity were systematically assessed to ensure the measure accurately reflects the intended theoretical constructs.

Convergent Validity was confirmed by examining factor loadings and the Average Variance Extracted (AVE). Most CFA factor loadings exceeded the standard threshold of 0.7, with only one item falling slightly below (0.674), which was retained due to its proximity to the threshold (Chen & Tsai, 2007). Crucially, all AVE values exceeded the recommended 0.5 threshold, providing robust evidence that the items strongly relate to their hypothesized constructs.

Discriminant Validity was supported by examining the relationship between the AVE values and the squared inter-construct correlations, ensuring that each factor measured was empirically distinct from the others within the overall measurement model.

Reliability

The reliability of the 22-item measure was assessed using internal consistency metrics. The primary indicator utilized was Composite Construct Reliability (CCR). Consistent with high psychometric standards (Hair et al., 2009), all CCR values obtained for the latent constructs exceeded 0.8. This finding confirms the strong internal consistency and dependability of the ACATAM across all measured factors, suggesting the scale items consistently measure the underlying constructs.

Factor Analysis

The hypothesized factor structure was tested using Confirmatory Factor Analysis (CFA). The analysis led to a respecified model to achieve optimal fit, which confirmed the robust structure of the seven latent factors.

Key fit indices supported the model adequacy: the Chi-square to degrees of freedom ratio (χ²/df) was 1.97; the Comparative Fit Index (CFI) was 0.960; the Tucker-Lewis Index (TLI) was 0.951; and the Root Mean Square Error of Approximation (RMSEA) was 0.050 (Toh et al., 2006). These values collectively demonstrate that the measurement model is statistically sound and aligns well with established psychometric standards for complex models.

• Perceived ethics
• Perceived benefits
• Perceived risks
• Perceived loss of competence
• Perceived unpredictability
• Convenience orientation
• Acceptance of technology agency

Instrument

Test Type: Inventory/Questionnaire (22-item measure)

Format: Items rated using a five-point Likert scale.

Language Available: English (based on administration to U.S. hotel guests)

Population Group: Human (Male and Female)

Age Group: Adulthood (18+), spanning Young Adulthood (18–29), Thirties (30–39), and Middle Age (40–64).

Population Details: Location: United States. Respondents: Hotel Guests.

Test Methodology: Test Validity, Convergent Validity, Discriminant Validity, Test Reliability, Internal Consistency, Factor Analysis, Confirmatory Factor Analysis, Measurement Model.

Keywords

Consumer Technology Acceptance, AI Decision-Making, Psychometrics, Composite Construct Reliability, Convergent Validity, Discriminant Validity, Hotel Guests, Artificial Intelligence, Perceived Ethics

Authors

Author ORCID Identifier: Cristian Morosan: 0000-0001-6751-1348; Aslıhan Dursun-Cengizci: N/A

Affiliation Email addresses: Not provided in source material.

Correspondence Address: Not provided in source material.

Permissions & Fee and Test Year

The scale was developed and published in 2024. Information regarding specific permissions and licensing fees for commercial or extensive academic use beyond standard citation should be sought directly from the corresponding author, Cristian Morosan, via the publication source.

Test Year: 2024

Reference’s

1. Morosan, C., & Dursun-Cengizci, A. (2024). Letting AI make decisions for me: An empirical examination of hotel guests’ acceptance of technology agency. International Journal of Contemporary Hospitality Management, 36(3), 946–974. DOI: 10.1108/IJCHM-08-2022-1041
2. Bertrandias, L., Cadenat, S., & Stenger, A. (2021). What drives consumers’ acceptance of artificial intelligence in service? The role of perceived risk, benefits, and control. Journal of Services Marketing, 35(6), 738–750.
3. Chen, S. H., & Tsai, Y. L. (2007). The impact of internal marketing on the relationship between organizational culture and organizational commitment. Journal of Business Research, 60(11), 1101–1109.
4. Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2009). Multivariate data analysis (7th ed.). Prentice Hall.
5. Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903.
6. Roman, S. (2007). The ethics of online retailing: A scale development and validation. Journal of Business Ethics, 72(2), 195–205.
7. Toh, K. W., Tsang, E. W. K., & Lim, K. K. (2006). The effects of organizational justice on employees’ commitment to change: The moderating role of procedural justice perception. Journal of Organizational Behavior, 27(8), 1085–1105.
8. Xu, X., Benbasat, I., & Cenfetelli, R. T. (2011). The nature and consequences of trade-off transparency in electronic commerce. Information Systems Research, 22(2), 241–257.

Items of the Antecedents of Consumer Acceptance Technology Agency–Model

IMPORTANT: The following scale items must be preserved in their original language and must not be changed in any way.

The original items constituting the 22-item measure were not provided in the source material. The scale covers seven factors: Perceived ethics, Perceived benefits, Perceived risks, Perceived loss of competence, Perceived unpredictability, Convenience orientation, and Acceptance of technology agency.