System Characteristics, Perceived Benefits, Individual Differences and Use Intentions

Using the Technology Acceptance Model (TAM) as a theoretical framework, this study investigates the effect of a set of individual differences (computer self-efficacy and knowledge of search domain) and system characteristics (relevance, terminology, and screen design) on intention to use digital libraries. The aim of this study is to test the correlation between perceived ease-of-use variable and perceived usefulness variable, beliefs (perceived ease-of-use and perceived usefulness) against behavior intention to use. This study also to test the relationship between individual differences (computer self efficacy and knowledge of search domain) and system characteristics, which consist of relevance terminology and screen design against belief variables. The study replicates Technology Acceptence Model (TAM) by Hong et al (2002) and implements in e-library area. Based on a sample 100 users of a university’s Gadjah Mada digital library, the results strongly support the utilization of TAM in predicting users’ intention to adopt digital libraries, and demonstrate the effects of critical external variables on behavior intention through perceived ease of use and perceived usefulness. All of the individual differences and system characteristics have significant effects on perceived ease of use digital libraries. In addition, relevance has strongest effect on perceived usefulness of digital libraries. Keywords: Individual differences, system characteristics, Technology Acceptance Model (TAM) perceived ease-of-use, perceived usefulness, e-library.

To the Editor, I read with interest the recent review article by Stabile and Cooper that identifies the lack of clinician involvement in the implementation, planning, design, and installation of anesthesia information management systems (AIMS) as a barrier to the adoption of health information technology. Many challenges are associated with the implementation of new AIMS in the perioperative setting, including the perception that workload will increase or autonomy will decrease. Even if AIMS use were mandated and early acceptance occur, the negative consequences of resistance may surface over the long term if the information system were viewed as prone to error or slow. Medical directors and anesthesiology department chiefs must actively manage the change process and gain physician acceptance for the new information system. Failure to gain acceptance of the AIMS may result in the unsuccessful launch of the system and huge financial losses. The Technology Acceptance Model, the most widely accepted method of adopting information technology, focuses on interventions taken by managers to change employees’ attitudes toward information technology. The model identifies two main factors that influence an individual’s intention to use information technology, namely, the extent to which the user believes that the information technology will enhance their job performance (perceived usefulness) and require little effort (perceived ease of use). Perceived usefulness is determined by social influence and system characteristics, and perceived ease of use is determined by facilitating factors, individual differences, and system characteristics. Social influence variables include the opinions about use of information technology from people who are important to the user (subjective norm) and the extent to which the user’s standing among their peers will be maintained or raised (image). System characteristics are defined by the degree to which the user thinks the information technology is important to their work (job relevance), effective (output quality), able to deliver results that are easy to observe and share (result demonstrability), enjoyable (perceived enjoyment), and matches their desired level of effort (objective usability). Facilitating factors refer to the user’s belief that the organization supports the information technology and has committed resources to maintain its operation (perception of external control). Individual differences are defined by the degree to which the user believes in their computer skills (computer self-efficacy), is apprehensive about using a computer (computer anxiety), and is motivated to use new technology (computer playfulness). Managers should take well-defined steps to target the determinants of perceived usefulness and perceived ease of use in order to improve the adoption of AIMS and influence acceptance of information systems among anesthesiologists.

A critical assumption of the Technology Acceptance Model (TAM) is that its belief constructs - perceived ease of use (PEOU) and perceived usefulness (PU) - fully mediate the influence of external variables on IT usage behavior. If this assumption is true, researchers can effectively "assume away" the effects of broad categories of external variables, those relating to the specific task, the technology, and user differences. One recent study did indeed find that belief constructs fully mediated individual differences, and its authors suggest that further studies with similar results could pave the way for simpler acceptance models that ignore such differences. To test the validity of these authors' results, we conducted a similar study to determine the effect of staff seniority, age, and education level on usage behavior. Our study involved 106 professional and administrative staff in the IT division of a large manufacturing company who voluntarily use email and word processing. We found that these individual user differences have significant direct effects on both the frequency and volume of usage. These effects are beyond the indirect effects as mediated through the TAM belief constructs. Thus, rather than corroborating the recent study, our findings underscore the importance of users' individual differences and suggest that TAM's belief constructs are accurate but incomplete predictors of usage behavior.

Integrated pest management (IPM) prioritizes non-chemical alternatives for managing pests and/or diseases, reserving chemicals as last solution, when economically and environmentally justified. The holistic consideration of the agroecosystem makes decision-making in crop protection more complex, as it requires selecting the most appropriate action at the right time. Decision support systems (DSS) for IPM help manage the risks of pests and diseases affecting crops by predicting potential threats and suggesting suitable control measures. However, their adoption remains modest among farmers, despite demonstrated benefits. An anonymous online survey was conducted among farmers, advisors, and other agricultural stakeholders to investigate factors influencing DSS uptake, or lack thereof. The survey distinguished between DSS users and non-users, enabling targeted questions on different aspects of DSS use and adoption. In total, 78 respondents were classified as DSS users and 29 as DSS non-users . Data were analyzed through clustering, followed by statistical validation of clusters and induction of a classification tree, to identify the most influential features shaping cluster formation among both DSS users and DSS non-users . The analysis identified four clusters in the DSS users’ dataset and two clusters in the DSS non-users’ dataset. For DSS users , DSS output reliability and usefulness were identified as the most influential features. Membership in a farmers’ network and DSS ease of use also contributed to cluster separation. For DSS non-users , DSS availability for different cropping systems was the most important attribute determining cluster separation. The results indicate that farmer involvement in DSS development and calibration may enhance trust and, therefore, adoption. Moreover, the need for user-friendly systems providing simple and clear information was highlighted. Although the lack of DSS tailored to specific cropping systems emerged as the main barrier, limited awareness and concerns about costs were also important obstacles. The applied methodological approach presents a reproducible and robust AI-based framework, built on open-source tools, to analyze stakeholders’ perceptions of IPM DSS and extract knowledge from complex survey data. This study provides valuable insights to facilitate DSS adoption for more sustainable crop protection and underscores the importance of both DSS technical performance and end-user needs.

The Engineering-Procurement-Construction (EPC) field is one of the complex industries that span the entire project cycle from bidding to engineering, construction, operations and maintenance (O&M). However, most EPC companies are exposed to contract-related risks during bidding or project execution period due to lack of data-based systematic decision-making system within limited time. In particular, in the client-supplied bidding document (ITB) in the EPC project, the client tends to pass the risk to the contractor. Therefore, when the client is participating in the bidding phase of a project, to analyze the contract (ITB) within a limited time and detect the presence or severity of risk sentence or clauses is of utmost importance. To analyze and detect the risk clauses of the bidding documents, professional experience and knowledge of the bidding documents is required, and it takes a lot of time and efforts to analyze and respond to the bidding documents that require complex sentences and expertise. In this study, it was performed as a preliminary step toward building an engineering decision support system. When conducting the EPC project, the items that could be risky were conceptualized by converting into a data base, and the main risk syntax and were constructed for algorithm. Text information was extracted from the bidding document (ITB) using syntax matching and named entity recognition technology for risk extraction, allowing users to systematically analyze and make a clear decision. In this study, research team applied to AI technology in EPC risk analysis especially phrase matcher and named-entity recognition (NER). Critical Risk Check Which is rule-based algorithm using phrase matcher method automatically extracts converts toxin clauses into a database. This Module contains 4steps as unstructured data Standardization, Pre-processing, Risk Database, Matching Algorithms. Terms Frequency Module using NER Model and EPC risk data was created in a similar syntax and converted into a JSON file. This package module identifying the frequency and location of the entity in the contract. The NER techniques can extract similar phrases of risky keywords and phrases. Also, can be demonstrated with domain characteristics such as location, general proper nouns as a frequency Image visualization. Through the Modules to be provided on decision-making support system as a cloud service. For the future works, research team improve the decision-making support system to present risk standards and semantic verification package.

Nowadays, due to the increasing presence of artificial intelligence in software systems, development teams face the challenge of working together to integrate tasks, resources, and roles in a new field, named AI Engineering. Proposals, in the way of models, highlight the needs of integrating two different perspectives – the software and the decision-making support (big data, machine learning, and so on) systems. But there is something more – both systems must achieve high quality levels for different properties; and this is not a straightforward task. Quality properties, such as reusability, traditionally evaluated and reinforced through modeling in software systems, do not exactly apply similarly in decision-making support systems. In this paper, we propose a model for managing reusable assets in AI engineered systems by linking software product line modeling and variety identification. The proposal is exemplified through a case study in the agriculture domain.

A theoretical acceptance model for computer-based communication media: Nine field studies

Decision making is a critical role that managers of organizations play and gives a bearing on the long-term survival of the organization. The study aimed to assess the influence of strategic decision making on organizational performance, taking the case of Rugarama Estate Park. Descriptive design was utilized with a population of 153 constituting of staff of Rugarama Estate Park as per the Human Resource records. A sample of 111 was utilized and purposive and simple random sampling approaches adopted in selecting the respondents to participate in study. The data was primary in nature collected using questionnaires. The study adopted theory of planned behavior. IBM SPSS Version 21 was used in analyzing data. The findings involved computing means, percentages, and frequencies, standard deviation and these were presented in tables. Descriptive statistics did reveal a mean of averagely 2 and less than 1 standard deviation in the case of advocacy decision making approach. However, dialogic and decision systems revealed an average mean of 4. Additionally, the corresponding coefficients for advocacy, dialogic and decision support systems were 0.260, 0.670 and 0.710 respectively. The regression coefficients for advocacy, dialogic and decision support systems were 0.092, 0.142 and 0.174 respectively with corresponding p values of 0.048, 0.002 and 0.001. This implied that advocacy decision making is not widely practiced while dialogic and decision support systems are widely practiced at Rugarama Estate Park. Study also concluded there was low positive significant association between advocacy approach and organization performance while dialogic and decision-making support systems had high positive association with organization performance. The null hypotheses were rejected hence a conclusion there was positive significant influence of advocacy, dialogic and decision support systems on organization performance. The study recommended independent and group thinking to be practiced in organization in decision making process, proposals regarding decision changes should be made available to members and every member given time to go through, all Stakeholders to be involved in decision making process to be able to feel part and parcel of the organization activities and lastly organizations to invest in technology systems that aids in speedy and efficient decision making process.. Keywords: Strategic Decision Making, Organizational Performance, Rugarama Estate Park, Rwanda

Background: The evolution of Artificial Intelligence (AI) has made it possible to create decision support systems to aid decision-making on timely, verifiable, and fact-based issues of immense importance and accuracy for public health administrators. Even with such interest, there are gaps in scholarly literature concerning the adoption, reliability, validity, and demographics of such systems. Objective: This research aims to analyze the adoption rate, the use of, and the reliability and validity of the decision support systems within the domain of public health, while assessing the demographic and professional differences in their use, and determining the most important predictors for their future adoption. Methods: This research was conducted with the use of quantitative cross-sectional methods to analyze the responses of 314 individuals in the public health domain, holding such positions as policy development, public health practice, healthcare provision, IT and AI analytics, and health data analysis. The participants answered a 20-item questionnaire, designed on a 5-point Likert scale that describes systems as a domain of public health and issues on supportive technologies embedded in public health (usefulness, ease of use, trust, organizational impact, and future readiness). Descriptive statistical framework systems, such as normality check, reliability Prism analysis (Cronbach’s Alpha) for community organization, validity (KMO and Bartlett’s test), and demographic computing (Independent Samples t-test, One-Way ANOVA, Kruskal–Wallis, Chi-Square test, Pearson correlation, and regression analysis) were employed. Results: The findings confirmed that the dataset was normally distributed with strong reliability (Cronbach’s Alpha = 0.87) and validity (KMO = 0.82; Bartlett’s χ² = 245.67, p < 0.001). Group comparison tests showed differences between the groups across gender, roles, and education levels. The inter-item correlation was strong and positive, while regression analysis showed that the most significant predictor of future adoption was trust and transparency (β = 0.42, p < 0.001), followed by perceived usefulness and organizational impact. Conclusion: AI DS DSSs are positively perceived and are expected to enhance the effectiveness of public health administration. The major barriers to adoption are demographic differences, trust, and lack of transparency. Demographic and trust barriers can be overcome with training and role-based approaches aimed at trust building, and they are essential for the successful adoption, sustainable future integration, and public health system performance of AI.

Topicality. Traditionally, routes were chosen based on the principle of the shortest logistical arm. But in the conditions of war, the impact of military risks and the dynamic nature of their change becomes much greater than the combined impact of other risks. This necessitates the use of reliability as the main objective function of transportation, a radical change in approaches to the rational selection of the most appropriate transportation options, and, in the event of a growing threat, the rapid change of the next stages of the cargo transportation route, even if the previous stages of the transportation route have already been completed.Aim and tasks. The aim of the work is the development of mathematical model for assessing the reliability of water transportation under military threats and the development of an algorithm for a decision-making support system for neutralizing the impact of threats to transportation. Achieving the goal of the research made it necessary to set and solve the following tasks: carry out a mathematical formalization of the problem and identify parameters of influence on the integral function of reliability; analyze the impact parameters and determine the most significant of them; to develop an algorithm for a decision-making support system for neutralizing the impact of threats to transportation; to determine effective ways of neutralizing threats. Materials and methods. Various methods were used in the work: content analysis to establish that the target reliability function can be calculated based on various features; comparative analysis to identify such features; mathematical formalization for the formation of a transportation reliability assessment model and the development of a decision-making support system algorithm for neutralizing the impact of threats to transportation; a steep ascent to choose the most expedient transportation options and, in the event of an increased threat, an alternative route option.Research results. A mathematical model for assessing the reliability of water transportation under military threats was developed and an algorithm for the decision-making support system was formed to neutralize the impact of threats to cargo transportation. For this solution, the following tasks were performed: mathematical formalization of the problem was carried out and parameters of influence on the reliability function were selected; influence parameters were analyzed and the most significant of them was determined; the algorithm of the decision-making support system for neutralizing the impact of threats to transportation was developed; defined ways of neutralizing threats.Conclusion. It is indicated that the risks associated with the aggression of the russian federation are absorbing in relation to all other risks. It has been established that due to the dynamic changes in the risks of cargo transportation in the conditions of war, the task arises not only of identifying the magnitude of the risk, but also its rate of change, because identifying the rate of risks change makes it possible to forecast it. It was established that during military operations, the function of reliability becomes the main target function of managing the transportation of goods by water transport. It was established that in the time preceding large-scale hostilities, the main risk is the time of forced delay of ships, therefore the reliability water transportation integral function of goods is inversely proportional to the time of ships delay. Using the developed model, the value of the reliability function and its changes in January 2022 were determined.

Decision-making support systems in railway transport are systems that make it easier for traffic controllers and dispatchers involved in the regulation of train traffic to make individual decisions more easily and accurately. Without such systems, dispatchers usually make decisions based on previous experiences and feelings they have developed working in train traffic control. However, quality decision-making support systems are based on large amounts of data processed by one or several different artificial intelligence techniques. This paper will examine the architecture of such a system in railway transport, which helps the dispatcher to make decisions based on different criteria and values of individual criteria. The architecture of this decision-making support system has been developed to equal or, if necessary, use the maximum available double-track railway line capacity to resolve delays caused by lack of capacity for any given route. This system has been developed for the specific configuration of a double track, whereby each track is intended for one direction of train traffic. This paper will lay the foundation for understanding decision-making support systems and for the development of a specific model of decision-making support system in practice.

The Sars-CoV-2 pandemic resulted in limited space for the public to interact and carry out their daily routines. The limited space available forces everyone to be more creative in meeting their daily needs. With currently developing technology, new information ecosystems have been developed to address problems especially in critical fields such as health, the economy and education. Artificial intelligence is now very commonly implemented in various fields, Decision Support Systems (DSS) are AI which are often found in most systems that are widely used. Many studies have been conducted using DSS. However, only a few are focused on overcoming the support system for the pandemic that has occurred since late 2019. Most of these DSS methods use outranking methods for multi-criteria parameters owned by users who require wise decisions to be decided. The outranking method has been developed in this article with the aim of obtaining a self-diagnosis system for patients with covid types of Alpha (B.1.17), Beta (B.1.351), Gamma (P.1), Delta (B.1.617.2), and Omicron (B.1.1 529) this detection system was developed using Electre and Promethee methods. Both methods utilize outranking to obtain detection results based on multi-criteria decisions of the symptoms experienced by the patient. From the system that has been developed, it is obtained data that is quite promising for the implementation of this outranking system, namely for both systems to obtain an accuracy of approximately 90% and more. This data indicates that the role of artificial intelligence, especially the decision system, is a method that is reliable enough to be implemented in MCDM (Multi-Criteria Decision Making).

In all fields of human society, occasional emergencies are almost inevitable. Once an emergency occurs, rapid and proper decision making is required. The purpose of this paper is to explore the design and development of computerized support systems for emergency decision making (EDM). First the characteristics of EDM problems are examined. Then, in view of limited human computer rationality, requirements for a computerized support system for EDM are determined. A conceptual structure for knowledge-based distributed emergency decision support systems is proposed. Finally, a prototype system for safety protection and disaster response in coal mines, developed using the proposed structure, is briefly described.

Purpose This study aims to design and validate a decision support system (DSS), named preliminary risk assessment of brownfield sites (PRABS). It is intended that the proposed DSS will aid the identification of potential hazards and, in doing so, highlight challenges facing those stakeholders dealing with the decision-making on brownfield site redevelopments, where the examples of diverse stakeholders would include, for instance, risk assessors, local planning authorities, regulator, developers, civil engineers, architectures, landowners, investors and alike. Moreover, the DSS will enable them to promote safer redevelopment and minimise the risks to future occupants of brownfield sites and neighbouring lands, on the top of the tool being communal platform of an effective communication between them as it is for both experts and non-experts. Design/methodology/approach This research employs a comprehensive five-stage process, integrating both quantitative and qualitative methods and utilizing mixed methods for a nuanced exploration of data. The initial stage involves an in-depth examination of contemporary risk assessment tools for contaminated sites, setting the foundation and benchmarks for subsequent stages. Stage two focuses on creating a conceptual framework using insights from existing literature to guide the development of the DSS tool. Stage three introduces a validation mechanism through a questionnaire administered to experts. Stage four involves the active development of the DSS tool, transforming theoretical constructs into a practical application. The final stage, stage five, employs quantitative data analysis and case studies to validate, refine and enhance the DSS tool’s applicability in real-world scenarios, ensuring its approval. Findings This study presents PRABS, a user-friendly DSS for the PRABS. Validation through a quantitative online survey indicates strong support for PRABS, with around 80% of participants willing to recommend it due to its ease of use and information quality. Qualitative data analysis using real-life case studies further demonstrates the tool’s effectiveness. PRABS proves valuable in identifying hazards during the preliminary stage, accurately predicting potential contaminants despite limited input data in the case studies. The tool’s hazard identification aligns well with expert judgments and case study reports, confirming its practical utility. Practical implications This study has several limitations. First, the DSS identifies only hazards associated with one layer of site geology, even though sites may include multiple layers, which limits the comprehensiveness of the hazard identification process. Second, adopting an online survey approach posed challenges in achieving a high response rate and gathering a representative sample, making it uncertain how the results might vary with a higher number of professional participants. This limitation affects the generalisability of the findings. Finally, while this study identified 65 potential hazards associated with brownfield sites, this number could be expanded to include hazards related to plants, animals and air, indicating the need for a more inclusive approach to hazard identification. Given these limitations, future research should focus on addressing these gaps. Originality/value The contributions of this study offer practical benefits. Firstly, it enables the initial risk assessment process to be more comprehensive and integrated and reduces complexity in the risk assessment process by ensuring that all probabilities, along with their significance, are identified at the initial stage of the risk assessment. This could be a strong starting point for successfully conducting a more detailed risk assessment and remediation. Secondly, the developed PRABS can promote effective environmental communication among stakeholders, which should speed up the planning process and help develop brownfield sites more efficiently and effectively, while preserving the natural environment.

Background An impressive amount of evidence exists on the effectiveness of guideline-based exercise for cardiac rehabilitation and secondary prevention. Also, studies show that exercise prescriptions are sub-optimal and not compliant with ESC/EAPC exercise guidelines. Decision support systems (DSS) for guideline-based exercise prescription, such as the EAPC endorsed EXPERT tool, have the potential to implement the guidelines in daily practice in an accessible way. This follows the assumption that a DSS for exercise prescription is readily adopted in clinical practice. Purpose While the general usability of the EXPERT tool has iteratively been studied and improved, no former study investigated its associated technology acceptance. The current study hypothesized that the technology acceptance of a DSS is influenced by internal and external factors, and by perceived barriers. Methods The technology acceptance (TA) study was embedded in a study on exercise prescription compliance, and on the training effect of the EXPERT tool. The intervention in this prospective, non-randomized intervention study was a one-month training with the EXPERT tool. At baseline and post-intervention, prescription compliance for three fictive patient cases with different complexity, with the ESC/EAPC guidelines was assessed. At the same points in time, questionnaires on TA (a modified TAM questionnaire) including possible barriers for adoption of clinical DSS, were presented to the participants. Results The current data analysis focused on findings in 24 participants that completed the study with the EXPERT training tool, out of 122 initial participants. All of them were Belgian physiotherapists, with varying experience. 15 participants (62.5%) were female, and the majority (66.7%) was younger than 31. 15 participants (62.5%) worked in a hospital, 5 of them were also involved in a private practice. No significant differences were found in responses to the TA questionnaire before and after the intervention. Significant negative correlations were found between the employment in a hospital and the "Perceived usefulness" (p=0.001), "Perceived ease of use" (p=0.019), "Attitude" (p=0.002) and "Subjective norm" (p=0.007). "Technological infrastructure in the workplace" was ranked as the main external barrier to usage of a DSS for guideline-based exercise prescription. Next on the list were "Time", "Evidence on the effectiveness of the system", "Organizational structure" and "Compatibility with standards of practice". The highest ranked internal barrier was "Experience" followed by "Familiarity" and "Knowledge". 16 out of 24 participants (66.7%) indicated that they are not aware of existing clinical DSS. Conclusion The results reveal that organizational factors and barriers are more decisive to technology acceptance than individual beliefs or technological attributes of the DSS. Aligning organizational practices with ESC/EAPC guidelines is essential and should be ambitioned.