Individual’s Intentions to use Self Diagnostic Medical Support Systems

In this paper, we consider adding the construct of trust to the Unified Theory of Acceptance and Use of Technology (UTAUT) model in the context of individual use of self-diagnosis medical support systems. We believe that these are important factors in an individual and personal setting. By the adding the concepts of knowledge-based trust, system-based trust, and belief-based trust to the UTAUT model we will be able to increase our understanding of individuals intention to use medical expert systems such as self-diagnosis medical systems. We feel that our conceptual model will have implications for medical system providers and government agencies as well as future academic researchers studying individual use of technology within a global/national context

Assessment of the Building Situation Tool adoption among firefighters

Abstract. Technology and technical tools have become standard resources that first responders use in their work. Throughout an incident, technology serves to improve communications, planning, safety, situational awareness, and decision-making. Certain incidents require specialized tools to resolve the crisis, whether it is for the law enforcement, medical, or firefighter unit to manage. One under-utilized technology is building sensors, recording information on temperature, CO2, smoke, airflow, and movement in the building. While modern buildings include sensors to monitor for potential dangers, that information is not shared with the fire department beyond notification of a fire alert. Despite the considerable number of hardware and software solutions adopted, firefighters in Kainuu, Finland still rely on paper plans when examining indoor disasters. The Building Situation Tool (BUST) was developed to utilize the building sensors and visualize the building as a 3D model, to provide firefighters with a realtime overview of the site during emergencies. The purpose of this study is to investigate the technological competencies of firefighters, determine the usability and ease of use of BUST, and examine the factors that influence the adoption of BUST. The constructs of the Technology Acceptance Model (TAM), selfefficacy, and workplace learning are used. These three constructs provide insight into how the intention to use technology is modeled, how users perceive their knowledge and use of technology, and how the workplace influences learning and performance. A mixed-method approach was used in this study. The firefighter’s technology self-efficacy, perceived usefulness, and ease of were recorded through quantitative questionnaires. The firefighter’s experiences in using the technology and factors that influence adoption were recorded through a questionnaire and interview. The findings show a sufficient level of competency, that first-time users prefer guided instructions, clarity in the user interface, controls, and options to customize the user interface. The findings have practical implications for the future development of BUST and its adoption in the workflow of firefighters

Tourist adoption of mapping apps: a UTAUT2 perspective of smart travellers

Purpose – Mapping apps are location based travel apps used for navigation and routing. These apps are gaining worldwide popularity because of its enormous potential. Despite of the growing popularity and utility of the mapping apps, the published literature in this area is scarce, leaving an unexplored area of research. Thus, the current study aims to identify factors affecting tourist’s intentions to use mapping apps while travelling. Design – The Extended Unified theory of acceptance and use of technology (UTAUT2) was applied as the basis of the present study Methodology – The data was collected from 284 travellers in India using a structured questionnaire. The data was analyzed using Partial Least Square approach. Findings – The results indicated that the most significant antecedents of behavioral intentions are habit, facilitating conditions, performance expectancy and hedonic motivation. It was observed that the actual usage behavior was influenced by traveler’s intentions and habit to use the technology (mapping apps). However it was noted that effort expectancy, social influence and price value had no significant effects on the tourist’s intentions to use mapping apps while travelling. Originality of the research – Till date limited empirical studies have explored the adoption of mapping apps by travelers. This study is unique as it explores the adoption intentions using a relevant theoretical framework in the developing economy context wherein the use of mapping apps is still in the nascent stage. This research contributes to the literature of innovation adoption and provides an interesting perspective to companies developing location based travel mobile apps

The Word Made Digital: Leveraging Artificial Intelligence to Increase Bible Engagement

The purpose of this descriptive study was to understand whether a relationship exists between an individual\u27s behavioral intention to use a Bible-based chatbot that leverages AI to create human-like engagement with Scripture and the constructs of performance expectancy, effort expectancy, perceived enjoyment, and perceived risk, controlling for gender, age, and experience among registered users of the Inductive Bible Study App. Data was collected through an online survey and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM), multi-group analysis (MGA), and homogeneity-of-slopes analysis of covariance (ANCOVA). While this quantitative descriptive study validated the correlation between each of the four reflective constructs and the formative construct (behavioral intent), the data suggests that perceived enjoyment maintains the strongest link to behavioral intent. In addition, the moderators appear to indicate that the strongest correlation to behavioral intent is found in communities of younger males with no prior exposure to chatbots. The results of this study provide useful insights into how individuals perceive and make decisions about using technology for religious or spiritual purposes, and how these perceptions may differ based on demographic factors. Additionally, the results inform the development and implementation of similar AI-based tools in religious or spiritual contexts and provide insights into how leaders in these contexts can effectively utilize technology to engage with their communities

Towards Human-centered Explainable AI: A Survey of User Studies for Model Explanations

Explainable AI (XAI) is widely viewed as a sine qua non for ever-expanding AI research. A better understanding of the needs of XAI users, as well as human-centered evaluations of explainable models are both a necessity and a challenge. In this paper, we explore how HCI and AI researchers conduct user studies in XAI applications based on a systematic literature review. After identifying and thoroughly analyzing 97core papers with human-based XAI evaluations over the past five years, we categorize them along the measured characteristics of explanatory methods, namely trust, understanding, usability, and human-AI collaboration performance. Our research shows that XAI is spreading more rapidly in certain application domains, such as recommender systems than in others, but that user evaluations are still rather sparse and incorporate hardly any insights from cognitive or social sciences. Based on a comprehensive discussion of best practices, i.e., common models, design choices, and measures in user studies, we propose practical guidelines on designing and conducting user studies for XAI researchers and practitioners. Lastly, this survey also highlights several open research directions, particularly linking psychological science and human-centered XAI

Mix method analysis for analyzing user behavior on logistic company mobile pocket software

The present study emphasizes mixed-method analysis, integrating the partial least square structural equation model (PLS-SEM) and customer journey for mobile pocket office improvement in logistic XYZ company. The extension of the unified theory of acceptance and use of technology (UTAUT 2) model by incorporating perceived risk (PR), personal innovativeness (PI), and trust (TR) variables are used. The sample for this study consisted of 243 res­pondents. Based on the results of the PLS-SEM analysis, two of the eleven tested hypotheses were determined to be rejected. In application usage, the proposed model effectively explained 85.7 per cent of the influence on beha­vioral intention (BI) and 72.1 per cent on use behavior (UB). The customer journey mapping (CJM) investigation's findings show that fluctuations in the use of mobile pocket office technology in the field are generally brought on by a lot of data entry, sluggish internet connections, and overworked field operations. The XYZ company may acquire sugges­tions and knowledge for developing further applications due to this inquiry.Saat ini, perkembangan teknologi komunikasi dan inovasi sangat penting bagi perekonomian. Selain itu, hal ini menyebabkan persaingan yang semakin ketat antara perusahaan. Aplikasi mobile pocket office berbasis mobile disediakan oleh perusahaan logistik PT. XYZ dalam upaya meningkatkan kualitas pelayanan khususnya di divisi operasional. Namun karena fluktuasi penggunaan, program mobile pocket office ini tidak dapat bekerja pada level puncaknya. Untuk menguji perilaku pengguna, penelitian ini menggunakan analisis metode campuran, mengintegrasikan PLS-SEM dan Perjalanan Pelanggan. Evaluasi PLS-SEM Penelitian ini menilai variabel yang mempengaruhi penerimaan pengguna terhadap penggunaan aplikasi mobile pocket office dengan membangun model UTAUT 2 yang ditingkatkan dengan menggabungkan variabel persepsi risiko (PR), inovasi pribadi (SINN), dan kepercayaan (TR). Sampel untuk penelitian ini terdiri dari 243 responden. Berdasarkan hasil analisis PLS-SEM, dua dari sebelas hipotesis yang diuji dinyatakan tidak benar. Efek terbesar pada niat perilaku dan perilaku penggunaan masing-masing disebabkan oleh variabel motivasi hedonis (HM) dan variabel kebiasaan (HB) (BI). Dalam konteks penggunaan aplikasi, model yang diusulkan menjelaskan secara efektif pengaruh sebesar 85,7 persen terhadap behavioral intention (BI) dan 72,1 persen terhadap use behavior (UB). Variabel persepsi risiko (PR) dan ekspektasi upaya (EE) diabaikan. Pengguna merasakan banyak usaha, dan tingginya risiko penyalahgunaan membuat mereka kurang tertarik menggunakan program, menurut hasil. Temuan investigasi Customer Journey Mapping (CJM) menunjukkan bahwa fluktuasi penggunaan teknologi mobile pocket office di lapangan umumnya disebabkan oleh banyak entri data, koneksi internet yang lamban, dan operasi lapangan yang terlalu banyak bekerja. PT. Perusahaan XYZ dapat memperoleh saran dan pengetahuan untuk mengembangkan aplikasi lebih lanjut sebagai hasil dari penyelidikan ini.   &nbsp

Quality Websites: An Application of the Kano Model to Website Design

In the emerging global electronic market, the creation of customer centered websites will become increasingly important. This paper uses Kano\u27s Model of Quality to develop a conceptual framework for investigating features in the web environment that satisfy basic, performance, and excitement needs of potential customers. The researchers classify features commonly used in the web environment according to Kano’ s three quality dimensions for products and services. Plans to empirically test this conceptually based classification are forthcoming. Among the possible implications and contributions of this research are the differentiation of web design features that customers take for granted from those that add value in the performance of web specific tasks and those that generate delight, motivation, and loyalty of website users

Reinvestigating millennial shopping behavior on the sharing economy platform: The moderating role of COVID-19 awareness level

Drawing from the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2), this study aims to develop a predictive model concerning the shopping behaviour of millennials within the realm of the sharing economy (SE) mobile application. To accommodate prior research findings while providing novelty, this study integrates hedonic enjoyment and price-saving orientation as predictive factors, alongside the level of COVID-19 awareness as a moderating variable. An online survey was administered, and primary data was collected by distributing an electronic questionnaire link randomly via email and social media platforms. Employing a sampling judgement technique, 260 millennials in Indonesia who utilize the SE (Gojek) mobile app were identified as participants. Results from the PLS-SEM analysis reveal that performance expectancy, effort expectancy, social influence,  price-saving orientation, and habits exert a favorable and significant impact on behavioral intentions. Furthermore, habits and behavioral intentions were found to significantly influence the actual usage of the SE app among millennials. Conversely, hedonic enjoyment demonstrated no significant influence on behavioral intentions. Moreover, the moderating role of COVID-19 awareness was observed to both enhance and diminish direct relationships. The implications, both theoretical and practical, along with recommendations for future research, are deliberated upon

Citizen science and remote sensing for crop yield gap analysis

The world population is anticipated to be around 9.1 billion in 2050 and the challenge is how to feed this huge number of people without affecting natural ecosystems. Different approaches have been proposed and closing the ‘yield gap’ on currently available agricultural lands is one of them. The concept of ‘yield gap’ is based on production ecological principles and can be estimated as the difference between a benchmark (e.g. climatic potential or water-limited yield) and the actual yield. Yield gap analysis can be performed at different scales: from field to global level. Of particular importance is estimating the yield gap and revealing the underlying explanatory factors contributing to it. As decisions are made by farmers, farm level yield gap analysis specifically contributes to better understanding, and provides entry points to increased production levels in specific farming systems. A major challenge for this type of analysis is the high data standards required which typically refer to (a) large sample size, (b) fine resolution and (c) great level of detail. Clearly, obtaining information about biophysical characteristics and crop and farm management for individual agricultural activities within a farm, as well as farm and farmer’s characteristics and socio-economic conditions for a large number of farms is costly and time-consuming. Nowadays, the proliferation of different types of mobile phones (e.g., smartphones) equipped with sensors (e.g., GPS, camera) makes it possible to implement effective and low-cost “bottom-up” data collection approaches such as citizen science. Using these innovative methodologies facilitate the collection of relatively large amounts of information directly from local communities. Moreover, other data collection methods such as remote sensing can provide data (e.g., on actual crop yield) for yield gap analysis. The main objective of this thesis, therefore, was to investigate the applicability of innovative data collection approaches such as crowdsourcing and remote sensing to support the assessment and monitoring of crop yield gaps. To address the main objective, the following research questions were formulated: 1) What are the main factors causing the yield gaps at the global, regional and crop level? 2) How could data for yield gap explaining factors be collected with innovative “bottom-up” approaches? 3) What are motivations of farmers to participate in agricultural citizen science? 4) What determines smallholder farmers to use technologies (e.g., mobile SMS) for agricultural data collection? 5) How can synergy of crowdsourced data and remote sensing improve the estimation and explanation of yield variability? Chapter 2 assesses data availability and data collection approaches for yield gap analysis and provides a summary of yield gap explaining factors at the global, regional and crop level, identified by previous studies. For this purpose, a review of yield gap studies (50 agronomic-based peer-reviewed articles) was performed to identify the most commonly considered and explaining factors of the yield gap. Using the review, we show that management and edaphic factors are more often considered to explain the yield gap compared to farm(er) characteristics and socio-economic factors. However, when considered, both farm(er) characteristics and socio-economic factors often explain the yield gap. Furthermore, within group comparison shows that fertilization and soil fertility factors are the most often considered management and edaphic groups. In the fertilization group, factors related to quantity (e.g., N fertilizer quantity) are more often considered compared to factors related to timing (e.g., N fertilizer timing). However, when considered, timing explained the yield gap more often. Finally, from the results at regional and crop level, it was evident that the relevance of factors depends on the location and crop, and that generalizations should not be made. Although the data included in yield gap analysis also depends on the objective, knowledge of explaining factors, and methods applied, data availability is a major limiting factor. Therefore, bottom-up data collection approaches (e.g., crowdsourcing) involving agricultural communities can provide alternatives to overcome this limitation and improve yield gap analysis. Chapter 3 explores the motivations of farmers to participate in citizen science. Building on motivational factors identified from previous citizen science studies, a questionnaire based methodology was developed which allowed the analysis of motivational factors and their relation to farmers’ characteristics. Using the developed questionnaire, semi-structured interviews were conducted with smallholder farmers in three countries (Ethiopia, Honduras and India). The results show that for Indian farmers a collectivistic type of motivation (i.e., contribute to scientific research) was more important than egoistic and altruistic motivations. For Ethiopian and Honduran farmers an egoistic intrinsic type of motivation (i.e., interest in sharing information) was most important. Moreover, the majority of the farmers in the three countries indicated that they would like to receive agronomic advice, capacity building and seed innovation as the main returns from the citizen science process. Country and education level were the two most important farmers’ characteristics that explained around 20% of the variation in farmers’ motivations. The results also show that motivations to participate in citizen science are different for smallholders in agriculture compared to other sectors. For example fun has appeared to be an important egoistic intrinsic factor to participate in other citizen science projects, the smallholder farmers involved in this research valued ‘passing free time’ the lowest. Chapter 4 investigates the factors that determine farmers to adopt mobile technology for agricultural data collection. To identify the factors, the unified theory of acceptance and use of technology (UTAUT2) model was employed and extended with additional constructs of trust, mastery-approach goals and personal innovativeness in information technology. As part of the research, we setup data collection platforms using open source applications (Frontline SMS and Ushahidi) and farmers provided their farm related information using SMS for two growing seasons. The sample for this research consisted of group of farmers involved in a mobile SMS experiment (n=110) and another group of farmers which was not involved in a mobile SMS experiment (n=110), in three regions of Ethiopia. The results from the structural equation modelling showed that performance expectancy, effort expectancy, price value and trust were the main factors that influence farmers to adopt mobile SMS technology for agricultural data collection. Among these factors, trust is the strongest predictor of farmer’s intention to adopt mobile SMS. This clearly indicates that in order to use the citizen science approach in the agricultural domain, establishing a trusted relationship with the smallholder farming community is crucial. Given that performance expectancy significantly predicted farmer’s behavioural intention to adopt mobile SMS, managers of agricultural citizen science projects need to ensure that using mobile SMS for agricultural data collection offers utilitarian benefits to the farmers. The importance of effort expectancy on farmer’s intention to adopt mobile SMS clearly indicates that mobile phone software developers need to develop easy to use mobile applications. Chapter 5 demonstrates the results of synergetic use of remote sensing and crowdsourcing for estimating and explaining crop yields at the field level. Sesame production on medium and large farms in Ethiopia was used as a case study. To evaluate the added value of the crowdsourcing approach to improve the prediction of sesame yield using remote sensing, two independent models based on the relationship between vegetation indices (VIs) and farmers reported yield were developed and compared. The first model was based on VI values extracted from all available remote sensing imagery acquired during the optimum growing period (hereafter optimum growing period VI). The second model was based on VI values extracted from remote sensing imagery acquired after sowing and before harvest dates per field (hereafter phenologically adjusted VI). To select the images acquired between sowing and harvesting dates per field, farmers crowdsourced crop phenology information was used. Results showed that vegetation indices derived based on farmers crowdsourced crop phenology information had a stronger relationship with sesame yield compared to vegetation indices derived based on the optimum growing period. This implies that using crowdsourced information related to crop phenology per field used to adjust the VIs, improved the performance of the model to predict sesame yield. Crowdsourcing was further used to identify the factors causing the yield variability within a field. According to the perception of farmers, overall soil fertility was the most important factor explaining the yield variability within a field, followed by high presence of weeds. Chapter 6 discusses the main findings of this thesis. It draws conclusions about the main research findings in each of the research questions addressed in the four main chapters. Finally, it discusses the necessary additional steps (e.g., data quality, sustainability) in a broader context that need to be considered to utilize the full potential of innovative data collection approaches for agricultural citizen science.</p

Using the UTAUT2 model to explain teacher acceptance of work performance assessment system

An organization needs qualified and experienced human resources. Performance appraisals are required to determine the effectiveness of their work. A performance appraisal system has already been created to objectively evaluate the work of teachers, principals, and teachers given additional tasks, and to provide instructions for developing the teaching profession, principals, and teachers given additional tasks. The goal of this study was to apply the unified theory of acceptance and use of technology 2 (UTAUT2) to explain why teachers accept the performance appraisal method. With the help of SmartPLS 3.2.8 software, a partial least square structural equation model (PLS-SEM) was used to analyze the data. The results showed that behavioral intention (BI) to utilize the system is influenced by performance expectancy (PE), social influence (SI), facilitating conditions (FC), and habit (HT). The findings also revealed that system use behavior (UB) is influenced by facilitating conditions (FC), habit (HT), and behavioral intention (BI). To increase the system's adoption, the studies suggest focusing on enhancing the system's ease of use and minimizing the system's flow complexity