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Research Article

Proposing Public IT Adoption with Its Theoretical Foundation and Empirical Findings

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DOI:

10.3791/73614

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September 15th, 2026

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1Seoul AI School, Seoul School of Integrated Sciences and Technologies (aSSIST University), 2School of Business, Sungkyunkwan University, 3Department of Management Information Systems, Dong-A University

In This Article

Summary

This study proposes the public IT adoption model, a prospect-theory-based perception-intention framework explaining how perceived gains and losses drive users’ acceptance or rejection of public IT services, such as airport biometrics. Two surveys, including one multinational survey, and expert interventions validated the model, supporting prospect theory across countries and contexts.

Abstract

This research proposes a theoretical model of public IT adoption (PITA). Public information technology (IT) services refer to IT-based services that public institutions offer to all citizens, such as e-government portals, public health applications, and biometric systems at airports. Because of public IT’s unique nature, previously developed models that focused primarily on the individual and firm levels have been deemed insufficient to identify the critical mass of public IT adoption. To address this gap, a perception-intention framework based on prospect theory was proposed to explore users’ perceived losses and gains in adopting a new public IT service, suggesting that these perceptions were the antecedents of either rejecting or accepting a novel public IT service. Two studies were conducted, including one with survey samples from three nations (South Korea, the United Kingdom, and the United States). Five industry experts were also invited, through an intervention-based research approach, to qualitatively refine and validate the model so that it could be used within a practical scenario. The results showed that the total value of acceptance, that is, the aggregate perceived value required for users to accept the service, computed as the sum of the relevant path coefficients, was consistently greater than that of rejection. A similar finding was seen for perceived gain, which was a stronger predictor of both acceptance and rejection than perceived loss. Although the findings rely on cross-sectional, self-reported data, they provide strong support for prospect theory and contribute to a further understanding of the theory’s fundamental role in public IT adoption. Thus, by extending prospect theory to the public-sector IT context, PITA offers researchers a validated theoretical foundation and practitioners a practical tool for diagnosing the adoption of public IT services.

Introduction

Information technology (IT) has evolved over the past few decades into a dominant and pervasive tool for pursuing innovation and has significantly altered everyday life. Since the introduction of IT adoption models based on psychological perspectives1,2,3,4, the relationship between users and IT has been thoroughly researched at the individual and firm levels5.

Meanwhile, many public organizations have struggled to achieve operational efficiency under overwhelming societal demands, such as airport congestion, which harms the effectiveness and quality of public services. Enlarging physical infrastructures might yield immediate benefits but requires significant investments of time and money6. In contrast, innovative public service providers have approached this issue through IT-driven innovation7, integrating innovations with current infrastructures to secure service quality and operational efficiency8,9,10,11. Public organizations are now embracing novel IT (e.g., cloud services, cybersecurity, and biometrics) to revitalize core assets, which creates additional adoption challenges12. This problem might result from an inadequate understanding of real users: prior research has predominantly focused on factors driving acceptance while neglecting rejection. Without users’ active involvement, IT implementations in public institutions may not achieve practical adoption.

However, public sector IT adoption presents unique challenges distinct from private sector contexts. Although “acceptance” and “rejection” appear to be at odds as two distinct status quos13, promoting one could inhibit the other, so both sides of the countervailing effect should be considered. Earlier studies of public IT service innovation tended to emphasize positive outcomes10,11,14,15,16. This paper, therefore, examined “acceptance” and “rejection” together, converging psychology-based IS models (i.e., diffusion of innovation and innovation resistance) with behavioral economics (i.e., prospect theory).

The case of airport biometrics, in which biometric identification replaces document-based procedures, was selected because this public IT service is offered to all citizens, involves sensitive personal information, and confronts users with a clear accept-or-reject decision. Two research questions guided the study. RQ1: How do perceived gains and losses influence users’ acceptance and rejection of a public IT service? RQ2: Is the total value required for acceptance larger than that for rejection, as prospect theory predicts? Accordingly, a fresh theoretical framework was put forth and validated by industry experts through the IBR (intervention-based research) strategy17 (Figure 1). This study thereby integrates prospect theory with IS adoption models and offers actionable insights for public IT managers.

Flowchart of research process: theory, model, data, analysis, significance, implications, expert review.
Figure 1: The research process of theoretical model framing and development. The diagram depicts the overall research flow, from theoretical model framing based on prior adoption models and prospect theory, through Study 1 and Study 2, each followed by an expert intervention, to the completion of the PITA model. Please click here to view a larger version of this figure.

IT adoption has been studied extensively: the technology acceptance model (TAM)2 and the unified theory of acceptance and use of technology (UTAUT)4 at the individual level, and the diffusion of innovation (DOI)1 and the technology-organization-environment framework (TOE)3 at the firm level, have spawned follow-up studies on augmented reality and language learning18,19, chatbots and digital payments20,21, mobility services22,23, and remote work24. However, these models have notable limitations for public IT: TAM assumes voluntary use, DOI neglects user resistance, and UTAUT was developed primarily for organizational settings, so their applicability to quasi-mandatory public IT services remains questionable. Studies of public IT adoption based on these models also exist, for instance, extending UTAUT to examine an online public grievance redressal system25 and applying TOE to human resource information systems in the public sector14; many further studies took similar approaches26,27,28,29,30,31,32,33,34,35.

However, IT adoption at the public level significantly differs from that at the individual or firm level (see Table 1): adoption entities and users are not the same, and users’ intentions are not obvious due to psychological inertia and the status quo of current services. The disruptive characteristics of IT may hinder restructuring existing systems36; adoption also requires changing users’ attitudes and behaviors, which demands a clear understanding of users’ perspectives. Given these characteristics, previous models emphasizing the individual and firm levels may not be adequate to pinpoint the critical mass for public IT adoption. Because IT-driven service innovation is critical for cost reduction and service enhancement37,38 and ultimately advances society39, a focused theoretical framework for public IT adoption is crucial.

Individual-levelFirm-levelPublic-level
Adoption entityIndividualsFirms (private)Firms (public)
UserIndividualsFirm employeesIndividuals
User intentionCertainCertain (forced)Uncertain
Psychological inertiaNegligibleNegligible (forced)Prominent
Value perceptionIndispensableIndispensable(forced)Indispensable
Main adoption purposePersonalEfficiency, cost-savingPublic interest
Relevant IT adoption modelsTAM2, UTAUT4DOI1, TOE3This study

Table 1: Different levels of IT adoption. The table contrasts IT adoption at the individual, firm, and public levels in terms of representative theoretical models, adopting entities, and users.

The existing literature has demonstrated that individuals and employees have an a priori (individual-level) or forced (firm-level) intention to use a specific IT service2,40, implying that adoption was predetermined4,41. However, not all users intend to use IT42, and only a few IS studies have identified the traits and justifications for rejection43,44. Accordingly, a new public IT service would deliberately be rejected until the innovation is either accepted or fails45.

Understanding users’ acceptance and rejection of a public institution’s IT service is the main goal of this study, examined through two value perceptions: perceived gain and perceived loss. Perceived gain was adapted from the diffusion of innovation model1,46; these constructs have been used extensively to examine perceptions favoring IT acceptance47,48 and show high validity across contexts40. In contrast, perceived loss includes the traits that explain why people preserve their current way of life despite pressures to adapt; grounded in innovation resistance42,49, this line of research examines why some people delay adopting an innovation or reject it outright49,50,51,52,53. A multidimensional approach captured these perceptions rather than treating loss and gain as general constructs.

Perceived loss was operationalized through five constructs53,54. Time loss (TIL) is the perceived time lost in learning and using the service, and when expectations are not met53. Social loss (SOL) is the risk of losing one’s standing in a social group by appearing foolish or out of touch54. Financial loss (FIL) covers potential monetary losses from purchase costs, fees, or personal-information theft54. Physical loss (PHL) is the perception that use could cause harm, discomfort, or exhaustion53,54. Performance loss (PEL) is the perceived decline in service quality or functionality compared with existing alternatives, including malfunction and failure to deliver the desired benefits53.

To explain perceived gain (PG), relative advantage, compatibility, trialability, and complexity were used1,46. Relative advantage (REA) is the degree to which an innovation is thought to be more advantageous than its forerunner; compatibility (COM), the degree to which it fits a potential adopter’s values, needs, and prior experiences; trialability (TRI), the extent of possible experimentation prior to adoption; and complexity (CPX), which is related to TAM’s perceived ease of use, refers to how difficult the innovation is to use and comprehend. Observability was excluded because it is too ambiguous to measure46 and because biometric outcomes in the public IT context are not readily observable by others.

The loss-gain framework was proposed to explain the perceived values that motivate reactions to a new public IT service. If the service is radical and threatens usual behavior, perceived loss was hypothesized to outweigh perceived gain, causing rejection; if this persists, rejection might become a constant aspect of behavioral intention42,49. Conversely, if perceived benefits outweigh anticipated losses, the user has few reasons to reject the service. Therefore, the following hypotheses were put forth:

H1. Perceived loss will have a positive influence on rejection.
H2. Perceived loss will have a negative influence on acceptance.
H3. Perceived gain will have a negative influence on rejection.
H4. Perceived gain will have a positive influence on acceptance.

Any IT-driven advancement in government services is intended to advance society (e.g., faster and safer boarding via airport biometrics), yet a recently launched public IT service is rife with risk and uncertainty, and users might not accept the benefits as intended. According to prospect theory55, people make such irrational decisions based on evaluations of alternatives, values, and attitudes toward uncertainty, judged relative to a reference point (reference dependence). The theory’s key insight is loss aversion, the tendency for losses to weigh more heavily than equivalent gains, together with diminishing sensitivity; this focus on gains and losses relative to the status quo aligns directly with the acceptance-rejection dichotomy examined here. Prospect theory has been used in recent IS and IT studies to explain irrational decisions under risk and uncertainty56,57,58,59,60, spanning contexts from AI-enabled work and digital health to public Wi-Fi and mobile payments. While these studies confirm the theory’s relevance, they focused primarily on acceptance intentions and neglected the rejection dimension central to the public IT context.

Users of public IT services act as though they decide to accept or reject the service based on the total values required (i.e., the sum of perceived gain and loss). No prior study on public IT adoption has investigated this phenomenon from this perspective. Accordingly, path coefficients (i.e., PL to REJ) were coded as values55, and the total values required for rejection and acceptance were calculated as follows:

V(R) = CLR + CGR      (1)
V(A) = CLA + CGA       (2)

where CLR, CGR, CLA, and CGA are the path coefficients of perceived loss to rejection, perceived gain to rejection, perceived loss to acceptance, and perceived gain to acceptance, respectively; these standardized coefficients yield V(R) and V(A) as composite values of the total influence of loss and gain. Because public IT services are designed to benefit users, while users weigh losses more heavily than gains when leaving the status quo, acceptance, the riskier choice, was expected to require a larger total value than rejection. Building on H1–H4, H5 tests the net balance of these effects:

H5. The total value required for acceptance, V(A), will be larger than for rejection, V(R). The resulting conceptual PITA (public information technology adoption) model is shown in Figure 2.

Decision-making process diagram; perceived loss and gain flow to rejection and acceptance.
Figure 2: Conceptual PITA model. The diagram shows perceived loss (PL) and perceived gain (PG) as antecedents of rejection (REJ) and acceptance (ACC); the paths a, b, c, and d correspond to the coefficients used to compute the total values V(R) and V(A). Note: a = CLR, b = CLA, c = CGR, and d = CGA  Please click here to view a larger version of this figure.

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Protocol

All study procedures complied with the ethical principles applicable to research involving human participants at aSSIST University, Sungkyunkwan University, and Dong-A University. Because survey responses were anonymized at the time of collection and no personally identifiable information was gathered, the Institutional Review Board of aSSIST University classified the study as exempt from full ethical review. The participating institutions did not obtain separate IRB approvals, as they relied on the exemption determination issued by the Institutional Review Board of aSSIST University, which covered both the research design and the data-collection procedures. This determination was made in accordance with Article 15 of the Bioethics and Safety Act of the Republic of Korea and the exemption criteria specified in Article 13 of its Enforcement Rule (https://public.irb.or.kr/pt/pt01/PT0103/PT0103R01.do). Prior to accessing the Qualtrics questionnaire, all participants were shown an electronic informed-consent statement explaining the academic purpose of the research, the anonymous processing of responses, the use of the data solely for research purposes, and the absence of reasonably foreseeable risks. Only participants who explicitly provided consent were allowed to proceed with the survey.

Overview of studies
Two empirical studies and two expert interventions were conducted to test the five hypotheses and the proposed PITA model. The results are shown in Figure 3. Two-wave surveys were conducted prior to COVID-19 (2019 Q4) and during COVID-19 (2020 Q2-Q3) to measure constructs separately at different times, reduce the possibility of common method bias (CMB) in the data, and increase the generalization of the study61,62.

Study comparison flowchart; pre/post-COVID, samples, regions; research timeline, interventions.
Figure 3: Overview of studies. The timeline summarizes the two-wave design: Study 1 (2019 Q4, before COVID-19, Korean sample, N = 322) followed by the first expert intervention, and Study 2 (2020 Q2-Q3, during COVID-19, Korea/UK/US samples, N = 602) followed by the second expert intervention. Please click here to view a larger version of this figure.

Study 1 model
The initial research model was created based on the developed hypotheses to reveal users’ intentions to accept or reject a recently introduced public IT service (i.e., biometrics at airports), as shown in Figure 4. The users’ opinions of public IT services were reflected in two second-order constructs, perceived loss (PL) and perceived gain (PG), each formed by its respective first-order constructs. These two second-order constructs serve as the antecedents of rejection (REJ) and acceptance (ACC), which represent users’ intentions to use public IT services.

Conceptual diagram; structural equation model with constructs PL, PG, REJ, ACC; research analysis.
Figure 4: Study 1 research model. Five first-order loss constructs (PEL, TIL, SOL, PHL, and FIL) form the second-order construct perceived loss, and four first-order gain constructs (REA, COM, TRI, and CPX) form perceived gain; the two second-order constructs are modeled as antecedents of rejection and acceptance (H1–H4). Please click here to view a larger version of this figure.

Study 1 research data
The first data sample, which was based on a total of 322 Korean participants (female: 46.6% and male: 53.4%) who had experience boarding an aircraft at least once within the past year, was gathered from November 13 to November 18, 2019 (prior to the COVID-19 pandemic beginning). Definitions and examples of public IT services (i.e., biometric applications at airports) were provided in the survey introduction. The raw data for Study 1 are provided in Supplementary File 1. Flight frequency was measured in five mutually exclusive categories (FF1–FF5), corresponding to one, two, three, four, and five or more flights in the past year (Study 1: 138, 91, 34, 21, and 38 participants; Study 2: 174, 153, 109, 82, and 84 participants, respectively).

The predictor constructs were evaluated using a seven-point Likert scale that ranged from “strongly disagree (1)” to “strongly agree (7)” (see Supplementary File 21,46,53,54,63,64 for the study questionnaire). After a pilot test with 20 participants, the revisions were limited to wording-level refinements of the question items to improve their stability, applicability, and ease of interpretation; no constructs or items were added or removed after the pilot. The supporting document contains the entire questionnaire as well as relevant statistics and theoretical context. Confirmatory factor analysis (CFA) and covariance-based structural equation modeling (CB-SEM) analyses were performed.

Experts’ intervention for Study 1
Five experts with at least ten years of experience were invited to ensure objectivity and improve the caliber of this work, including two from international organizations (Airports Council International and International Air Transport Association), two from governmental organizations (Singapore Changi Airport and Korea Airports Corporation), and one from a private company that offers services in a public setting (Korean Air). This expert intervention was essential in providing feedback to enhance the model and domain knowledge of the target setting. Both qualitative (i.e., subjective feedback) and quantitative (seven-point Likert scale) evaluations were carried out. A revised version of the decision-report-characteristics questionnaire items (see Supplementary File 3)65 was adopted for the quantitative assessment and validated using the intraclass correlation coefficient66. In each intervention round, every expert completed the quantitative questionnaire individually and then provided open-ended qualitative feedback; the same panel, instruments, and procedure were used in both interventions to enable replication.

Study 2
To address the issues identified in Study 1 and improve the model’s generalizability, Study 2 applied the same model as Study 1. Study 2 included 602 participants (female: 42.3%, male: 57.7%, Korea: 200, UK: 201, and US: 201). Data were collected between July 23 and August 19, 2020 (during the COVID-19 pandemic). The raw data for Study 2 are provided in Supplementary File 4. With 33 observed indicators, this sample size provides approximately 18 observations per indicator, exceeding the commonly recommended criterion of 10 observations per indicator for CB-SEM, and also exceeds the widely used minimum threshold of 200 observations. KR, UK, and the US were chosen because they accounted for 29.1% of all air travelers worldwide (roughly 1.6 billion of 5.5 billion) from July 2019 to June 2020. The COVID-19 pandemic affected these countries at different times, beginning in Korea and later affecting the US and the UK. Additionally, the three nations offered a blend of Eastern and Western cultural traditions. Consequently, the combined dataset from these countries was used to examine users’ perception-intention framework for the novel public IT service (i.e., airport biometrics).

Experts’ intervention for Study 2
The same experts were invited, the same quantitative and qualitative evaluations were carried out, and their feedback was solicited to validate the findings in Study 2.

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Results

Study 1
To assess potential CMB associated with self-reported data, the one-factor analysis was first carried out67. According to the test’s findings, one factor accounted for 36.56% of the variance; a single factor accounting for less than 50% of the variance indicates that CMB is not a concern62,67,68. The construct validity and reliability were then assessed. The findings in

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Discussion

This two-study investigation tested the PITA model of public IT adoption; across both studies, all five hypotheses were supported, and the gap between V(A) and V(R) widened in the riskier COVID-19 context, in line with prospect theory’s prediction that riskier choices require greater perceived value. A gap was found in the theoretical foundations of the IT adoption literature, including TAM2 and DOI1, which failed to adequately explain public IT adoption. Most importa...

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Disclosures

The authors declare no conflicts of interest. During the preparation of this revised manuscript, a generative artificial intelligence tool (ChatGPT 5o) was used to assist with language editing and formatting. The authors reviewed and verified all content and take full responsibility for the manuscript; no AI tool was used to generate data, perform analyses, or create figures.

Acknowledgements

This work was supported by research funding from aSSIST University.

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Materials

List of materials used in this article
NameCompanyCatalog NumberComments
IBM SPSS Amos (CB-SEM software)IBM Corp. (Armonk, NY, USA)version 27; https://www.ibm.com/products/structural-equation-modeling-sem/Used for all covariance-based structural equation modeling (CB-SEM) analyses, including measurement model assessment (CFA: composite reliability, AVE, and discriminant validity) and structural model path analysis. 
Prolific (Online Participant Recruitment Platform)Prolific Academic Ltdhttps://www.prolific.com/Online platform used to recruit survey participants. Participants were screened based on eligibility criteria (e.g., airport experience) and compensated according to Prolific's fair-pay guidelines. 
Qualtrics (Online Survey Platform)Qualtrics, LLCRRID:SCR_016728Web-based survey platform used for questionnaire design, distribution, and response data collection. All measurement items were administered via Qualtrics using a 7-point Likert scale (1 = strongly disagree, 7 = strongly agree). https://www.qualtrics.com/

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