User Beliefs Research Articles

Rasch analysis of the Back Pain Attitudes Questionnaire (Back-PAQ)

Context As psychosocial factors have been recognised as significant predictors of the recovery trajectory from chronic back pain, the 34-item Back Pain Attitudes Questionnaire (Back-PAQ) was developed based on themes obtained from patient interviews, but previous psychometric analyses with a general population sample revealed uncertainty around the factor structure of the instrument. Objectives To provide more detailed information about the psychometric properties of the Back-PAQ when used with participants from the general population and also to test the internal validity of the tool for use with General Practitioners (GPs). Methods After applying partial-credit Rasch analysis with a sample of participants from the general population (n = 600), a replication analysis was conducted with a sample of GPs (n = 184). This approach permitted examination of sample-specific personal factors for differential item functioning. Subtests were used to differentiate between local dependency due to underlying dimensionality from local dependency due to method effects. Results A unidimensional fit to the Rasch model was achieved after 14 misfitting items had been deleted. The final 20-item solution also fit with a sample of 184 GPs. In both cases, the Back-PAQ-20 demonstrated good reliability (PSI ≥ 0.80), with no evidence of differential item functioning by personal factors. Conclusion The ordinal-to-interval conversion algorithms presented here further enhance the precision of the scale and permit analysis of Back-PAQ-20 scores using parametric statistics. The present study provided evidence for valid and reliable assessment of the back pain recovery beliefs of both users as well as providers of health services. IMPLICATIONS FOR REHABILITATION Psychosocial factors have been recognised as significant predictors of the recovery trajectory from chronic back pain. The 34-item Back Pain Attitudes Questionnaire (Back-PAQ) was developed based on themes obtained from patient interviews, but previous psychometric analyses with a general population sample revealed uncertainty around the factor structure of the instrument. The 20-item version of the Back Pain Attitudes Questionnaire (Back-PAQ) is shown here to have strong psychometric properties for administration with users and providers of health services.

What drives FinTech adoption? A multi-method evaluation using an adapted technology acceptance model

PurposeConsidering the ubiquity of FinTech services, the study proposes a research framework to examine FinTech adoption and use from the technology acceptance perspective by adding sub-constructs of technology acceptance model (TAM), unified theory of acceptance and use of technology (UTAUT), ServPerf and WebQual 4.0. This study broadly classified these sub-constructs in three dimensions: adoption, behavior and technological and explores the relationship between these attributes. It also proposes that digital behavior (Internet experience and level of awareness) and demographic characteristics (age and gender) moderate the main relationships.Design/methodology/approachThe measurement scale for the study is developed through iterative discussion with domain experts. The data are collected from 439 active Internet users though a digital survey and analysis were done by applying structural equation modeling and multi-group analysis.FindingsPerceived usefulness and social influence are found to be the key determinant for behavior intention to use FinTech services, with social influence having significant negative influence. Actual use is significantly influenced by ease of use and social influence but is not determined by behavior intention and perceived usefulness. Behavioral attributes are significantly impacted by technological attributes and digital behavior. Also, age significantly affects the perception of security among older users.Practical implicationsThis study will help FinTech service providers to design FinTech services considering a wide spectrum of users. More consideration should be on enhancing the usefulness and security features to create social affirmations for the use of FinTech services. This will entice users for frequent use and attract nonusers to do their first online financial transaction.Originality/valueThe study adds to the technology acceptance literature by incorporating relevant technological and behavioral attributes and investigating the moderating effect of digital behavior and demographic characteristics. It contributes to the understanding of user beliefs and perceptions about actual use of FinTech services.

Middle-Aged Video Consumers' Beliefs About Algorithmic Recommendations on YouTube

User beliefs about algorithmic systems are constantly co-produced through user interaction and the complex socio-technical systems that generate recommendations. Identifying these beliefs is crucial because they influence how users interact with recommendation algorithms. With no prior work on user beliefs of algorithmic video recommendations, practitioners lack relevant knowledge to improve the user experience of such systems. To address this problem, we conducted semi-structured interviews with middle-aged YouTube video consumers to analyze their user beliefs about the video recommendation system. Our analysis revealed different factors that users believe influence their recommendations. Based on these factors, we identified four groups of user beliefs: Previous Actions, Social Media, Recommender System, and Company Policy. Additionally, we propose a framework to distinguish the four main actors that users believe influence their video recommendations: the current user, other users, the algorithm, and the organization. This framework provides a new lens to explore design suggestions based on the agency of these four actors. It also exposes a novel aspect previously unexplored: the effect of corporate decisions on the interaction with algorithmic recommendations. While we found that users are aware of the existence of the recommendation system on YouTube, we show that their understanding of this system is limited.

Evaluation of the e-Learning Utilization as a Learning System in Higher Education Institution

This study aims to evaluate the use of technology to support teaching and learning activities. Lecturers and students have applied e-learning to teach subjects. The purpose of this evaluation is to measure the success of the use of STMIK Bumigora e-learning by using the Technology Acceptance Model (TAM) approach, which is an approach that can explain user behavior towards the use of technology. Evaluation of the use of e-learning is formulated into a model based on the TAM model, while SEM (Structural Equation Modelling) is used for data analysis. Based on the measurement analysis in this study, several factors most influenced the effectiveness of e-learning, namely the usage tutorial for users, ICT facilities related to the Ease of accessing the internet network. Meanwhile, in structural analysis, it was found that attitudes toward the use and perceived usefulness were strongly correlated with real use factors. The actual use is a real condition of the use of e-learning measured by the frequency and duration of time in using the technology, which is influenced by the user's belief in accepting the existence of e-learning in STMIK Bumigora and user beliefs related to the benefits when using it. Therefore, attitudes toward the use and perception of usefulness are the main determining factors in measuring the frequency and duration of e-learning use.

Determinants of cyber-safety behaviors in a developing economy

In a world increasingly driven by digital technology, cyber-safety is becoming a pressing concern for Internet users. This article contributes to sociological, psychological, and criminological literature on digital risks by investigating the determinants of individuals’ cyber-safety behaviors. Our study adds new insights to digital inequalities studies, cognitive health behavior models, and fear of crime literature by developing and testing a comprehensive theoretical model in a developing economy (Uruguay). To validate our model, we fit structural equations with data from the 2017 WIP+DiSTO Uy survey, a representative sample of Uruguayans (N=653). We found that operational digital skills were the strongest predictor of cyber-safety, while also providing support for the sequentiality of the digital divide hypothesis, as education and age-based disparities affect cyber-safety through their effect on Internet use, which in turn affects digital skills. Additionally, findings contribute both to cognitive behaviors and fear of crime literature, by attesting that gender and age-based disparities only have an indirect effect on cyber-safety, which is mediated by user beliefs regarding the severity of cyber-victimization. The study also provides evidence for the generalization of cyber-safety behavior theories — originally formulated based on data from developed economies — to developing ones, by stressing the role of digital skills and perceptions of victimization severity as the main direct antecedents of cyber-safety.

Cross-cultural insights on lived experiences of opioid users among young adults

Purpose There is increased social discussion of the emerging issues of opioid use, health and well-being of young adults within contemporary Asian society. The purpose of this study is to contribute new knowledge of opioid-using young adults through ethnographic perspectives of the five main cultures of Pakistan. Furthermore, it tried to explore the values of opioid users. Design/methodology/approach It is a qualitative study. Semi-structured interview and observation techniques were used to gain the participants’ information, in a non-judgmental environment. Observation and life focus history interview methodology were used for data collection. The sample consisted of 18 male opioid users (approximately three from each area including: Punjab, Khyber Pakhtoon Khah, Balochistan, Sindh, Gilgit Baltistan and Azad Jammu and Kashmir). An interview script was used in interviews after written informed consent. After the detailed interview and observation of non-verbal behaviours, the researchers analysed the data by using the content analysis of qualitative method. Findings The result showed that poor relationships, conflicts over property, lack of social support and family problems increased with the use of opioids by individuals. The individual’s mental health, such as hopelessness, curiosity, mistrust and lack of interest in social activities, was the major cause of opioid addiction. Moreover, peer influences and friends may also have played contributing roles in opioids use among men. Research limitations/implications The current study added to the understanding of the relationship of different environmental, behavioural and social factors involved in developing opioid use among young individuals. The homogeneity of the sample may have weakened the generalisability of the findings of the study as all participants in the study were male. Practical implications Clinicians and allied professionals have shown a great interest in early intervention with opioid users. At the same time, there is a lack of qualitative studies exploring the lived experiences of young opioid users. That is why this study was done, to empower counsellors. Social implications Opioids, including heroin, have strong addictive tendencies. They are easily available, particularly in unplanned urban areas of Pakistan. The present study tried to understand the lived experiences and beliefs of opioid users belonging to different cultures of Pakistan. Originality/value At the same time, there is a lack of qualitative studies exploring the lived experiences of adult opioid users. Therefore, this study presented 18 interview-based facts from the opioid users belonging to different areas of Pakistan residing in capital of Pakistan. The content of these vignettes was examined in the context of an ethnographic perspective, as it has a strong connection and contribution to drug abuse.

Unlocking the smart home: exploring key factors affecting the smart lock adoption intention

PurposeThis study examines the factorial structure of salient user beliefs associated with smart locks. We also examine the predictive value of the identified constructs on the smart lock adoption intention and we evaluate gender differences in the predictive value of the identified constructs.Design/methodology/approachThe study assumes pragmatic epistemological stance and it leverages mixed-methods research design. The research progresses through three stages: belief elicitation, exploratory factor analysis and confirmatory factor analysis within a nomological network. New groups of participants were recruited for each stage of the study.FindingsWe find that while potential adopters express a broad range of perceived benefits and concerns associated with smart locks, only the perceived relative advantage of smart locks vis-à-vis conventional locks in providing safety and security is significantly correlated with adoption intention for both genders. We also find that perceived novel benefits are a significant predictor of the smart lock adoption intention for women, but not for men.Research limitations/implicationsOur results indicate that perceived relative advantage can be the singular critical consideration in the adoption of smart home technologies that replace incumbent solutions. The results also demonstrate that gender-specific models can better capture gender effects that influence technology adoption and use.Practical implicationsSmart home technology vendors would need to convince prospective users that new technology is better than the incumbent solutions on the core affordances of the incumbent technology. Men and women differ in the consideration of novel benefits afforded by novel technologies.Originality/valueThis is among the first studies to examine salient beliefs that affect smart home technology adoption. The findings suggest that the traditional models (TAM, UTAUT) do not capture the key salient beliefs that can influence innovative smart home technology adoption. The study also suggests that gendered models are needed to understand technology adoption in contexts where technology adoption intersects with gender roles.

Fulfillment of higher-order psychological needs through technology: The case of smart thermostats

Smart home technologies (SHTs), such as smart thermostats, are a growing commercial opportunity. SHTs can offer a broad spectrum of potential functional, experiential and esthetic benefits, yet much of the prior research on the adoption of SHTs has been limited by a narrow TAM-based functional view. Relatively little is known about the salient user beliefs related to the experiential and esthetic benefits of SHTs that may affect SHT adoption. To address this gap in research, we conduct a mixed-methods study, wherein we inductively develop a set of factors that reflect salient user considerations associated with smart thermostats and we examine the effects of the elicited factors on the smart thermostat adoption intention. We find that performance expectancy, emphasized by TAM, has a relatively minor effect on the smart thermostat adoption intention and effort expectancy has no effect at all. We identify a novel factor, which we term techno-coolness, as the key predictor of the adoption intention in this context. Techno-coolness is a multidimensional construct that encompasses the perceptions that the technology can make a home look modern and futuristic, can make the user feel technologically advanced, and can be fun to use. Techno-coolness reveals that the technology capacity to fulfill higher-order psychological needs can dominate purely functional considerations in innovative technology adoption decisions.

The Nature and Role of User Beliefs Regarding a Website's Design Quality

Researchers and practitioners have long been interested in identifying the criteria that users consider important in assessing whether a system is worth using. However, past research in this domain has not taken into account the characteristics of a system's design and their quality in a systematic and comprehensive manner, which is likely to have limited the development of actionable design guidelines. The article addresses this issue by suggesting a research model that links user beliefs—which have traditionally been used in IT acceptance and success research (i.e., information quality, system quality, usefulness, and ease of use)—to their beliefs regarding the quality of three categories of a system's design (i.e., visual quality, page layout quality, and navigation quality) and testing it in the context of organizational intranets. The analysis of data collected from 159 intranet website users in three organizations supported the model, suggesting that the three categories of design quality beliefs significantly influenced users' assessment of their system's information quality and system quality.

Usability of Mobile Applications: A Systematic Literature Study

Since the release of the first mobile devices, the usability of on-board applications has been the concern not only of software vendors but hardware manufacturers as well. The academia community later willingly joined the discussion on usability in terms of theory and empirical measurement, having experience and knowledge in desktop settings. At first sight, such a background should guarantee a solid foundation to conduct research on software usability in a new setting. However, a preliminary study on the subject matter revealed methodological disorder in contemporary literature. As a matter of fact, a need emerged to review existing usability definitions, attributes and measures to recognize all associated aspects. In order to fill this void, we conducted a systematic literature review on usability studies indexed by the Scopus database and devoted to mobile applications. The input volume covers 790 documents from 2001 to 2018. The data analysis shows that the ISO 9241–11 usability definition has been adopted in an unchanged form and popularized as the standard by the HCI community. Secondly, in total, 75 attributes were identified and analysed. The most frequent are efficiency (70%), satisfaction (66%) and effectiveness (58%), which directly originate from the above definition. Subsequently, the less frequent are learnability (45%), memorability (23%), cognitive load (19%) and errors (17%). The last two concern simplicity (13%) and ease of use (9%). Thirdly, in the evaluation of usability, controlled observation and surveys are two major research methods applied, while eye-tracking, thinking aloud and interview are hardly used and serve as complementary to collect additional data. Moreover, usability evaluations are often confused with user experience dimensions, covering not only application quality characteristics, but also user beliefs, emotions and preferences. All these results indicate the need for further research on the usability of mobile applications, aiming to establish a consensus in the theory and practice among all interested parties.

Social network service based on ABC theory

BACKGROUND: The rapid development of information technology has changed the interactive behaviors in people’s daily lives. OBJECTIVE: The purpose of this paper is to investigate as to whether the characteristics of the social network service (SNS) can increase the motivation (antecedent) of the SNS users; and in turn, increase their affection and cognitive (beliefs) towards the SNS; and then increase their purchase behavior (consequence). METHODS: This study uses the antecedent belief consequence (ABC) theory, and collects data from the SNS users in Taiwan, with a total of 188 valid surveys were completed for model testing. RESULTS: The findings show that: (1) The SNS characteristics (information and service qualities), indeed, have influenced the affection and cognitive of the SNS users, but the system quality can only influence the affection of the SNS users. (2) The purchase behavior of the SNS users have been influenced by the cognitive of the SNS users only, but not by the affection. CONCLUSIONS: The result demonstrates that the beliefs of the SNS users should be considered when the researchers investigate the purchase behavior of the SNS users. This paper also discusses the contribution to the research and the SNS management practice.

Refleksi Ajaran Shinto Dalam Omamori

(Title: Shintoism Reflection InOmamori) Shinto handed down from generation to the next generation. Shintoism have had a strong influence on the lives of Japanese people, from festival activities to objects in their surroundings. Omamori is known as one form of that influence and became a culture part of the harmonization of the Shintoism and Buddhism. As one of the sacred objects which are still trusted by Japanese people, the existence of omamori is so popular even in modern society. Through a literature review, this article aims to describe what parts of the omamori are a reflection of Shintoism. The results obtained show that the reflection can be seen among others in the omamori user's belief in the existence of kamisama (Gods), and evil spirits, also can be seen from the material of the omamori itself.

Your Stance is Exposed! Analysing Possible Factors for Stance Detection on Social Media

To what extent user's stance towards a given topic could be inferred? Most of the studies on stance detection have focused on analysing user's posts on a given topic to predict the stance. However, the stance in social media can be inferred from a mixture of signals that might reflect user's beliefs including posts and online interactions. This paper examines various online features of users to detect their stance towards different topics. We compare multiple set of features, including on-topic content, network interactions, user's preferences, and online network connections. Our objective is to understand the online signals that can reveal the users' stance. Experimentation is applied on tweets dataset from the SemEval stance detection task, which covers five topics. Results show that stance of a user can be detected with multiple signals of user's online activity, including their posts on the topic, the network they interact with or follow, the websites they visit, and the content they like. The performance of the stance modelling using different network features are comparable with the state-of-the-art reported model that used textual content only. In addition, combining network and content features leads to the highest reported performance to date on the SemEval dataset with F-measure of 72.49%. We further present an extensive analysis to show how these different set of features can reveal stance. Our findings have distinct privacy implications, where they highlight that stance is strongly embedded in user's online social network that, in principle, individuals can be profiled from their interactions and connections even when they do not post about the topic.

Adoption of Social Media by Architecture Students in Fostering Community Service Initiative using Technology Acceptance Model

The use of the Internet for social networking is very popular amongst the youth. Collaborative technology leads to instant online community that enables fast and convenient communication. Adoption of Social Media has been researched on many contexts. However, little research has paid close attention to the student’s acceptance of Social Media (SM) as e-learning in higher educational teaching, especially in architectural education. This study aims to investigate factors that affect acceptance and behaviour of architecture undergraduate students towards the usage of SM. A quantitative method was used and qualitative responses of 33 architecture students using social media in their elective module, Community Service Initiative (CSI), was analyzed using the Technology Acceptance Model (TAM). TAM model for this study focused on social norm (SN) as an external factor, the two user beliefs, perceived usefulness (PU) and perceived ease of use (PEU) and behavioural intention to use (BI). Factor Analysis and Pearson Correlation showed significant relationship between social norm, perceived ease of use, perceived usefulness, and behavioural intention to use. The results of this study provided evidence for the potential use of social media as e-learning in similar modules.

The relationship between social sharing sites users’ personality beliefs and purposes of using social sharing sites of university student’s

The purpose of this study is to determine the relationship between site use intentions and personality beliefs of social network users. The Education faculty students at a private university in North Cyprus were informed about the research and the questionnaires were applied by the researchers to volunteer students. From total of 198 students, 143 (72.2%) were females and 55 (27.8%) males. Socio-demographic Information Form and Personality Belief Scale were used. The results show that participants who reported political opinion had significantly higher scores of schizoid, antisocial and narcissistic personality beliefs than those who did not. Multiple regression analysis was conducted to predict personality beliefs of purpose, sharing and profile information. When the t-test is examined, it is seen that commenting on only the shares of the subject variables is a significant predictor of the narcissistic personality belief. In this study, participants who have narcissistic, passive-aggressive, paranoid and antisocial personality beliefs usually and more frequently share songs on social networks. Keywords: Belief, personality, social networks, university students, social sharing

Applying an Implementation Framework to the Use of Evidence from Economic Evaluations in Making Healthcare Decisions.

There is a need for the application of theory in understanding the use of evidence from economic evaluations in healthcare decision making. The purpose of this study is to review the published literature on the use of evidence from economic evaluations for healthcare decision making and to map the findings to the Consolidated Framework for Implementation Research (CFIR). A systematic search strategy was used to identify studies investigating the factors that determine the use of evidence from economic evaluation in healthcare decision making. Barriers and facilitators identified in the included studies were mapped across the five CFIR domains, with the "intervention" referring to the use of economic evaluations in decision making. Gaps, inconsistencies and emergent relations were identified through the mapping process. Fifty-three studies met eligibility criteria and were included in the review. The CFIR constructs associated with the Intervention Characteristics and those associated with the knowledge and beliefs of users of economic evaluations were widely cited in the identified barriers and facilitators. Other constructs from the CFIR had not been reported in the literature, such as 'organisational networks' and 'individual stage of change'. Most of the stages in the implementation process as described by the CFIR were reflected in the identified barriers and facilitators. By categorising barriers and facilitators into domains, the CFIR provides a systematic approach to assess how these factors interact. Literature gaps in the literature regarding the use of economic evaluation in healthcare decision making were identified, specifically issues regarding organisational networks and the role of feedback. Through mapping findings from studies of the use of evidence from economic evaluations in healthcare decision making, we present an implementation framework based on the CFIR for understanding the use of economic evaluations into practice.

Belief - Based Marketing vs. Conjoint : An Illustration Using the Indian Mobile Phone Market

Many market research surveys conducted in the past have been engineering surveys and not genuine surveys of consumer beliefs. Brand managers should not only be able to simulate the market share of a newly designed product, but show the market share changes that result from changing consumer beliefs about existing products. The current research contrasted traditional conjoint with a variant that also collected data on consumer beliefs about what features a product has: the Boolean user belief (BUB) approach. This study compared BUB to the existing fixed data (FD) approach. Random assignment allocated 608 respondents one of the three methods: ACA, ACBC, and a self-explicated scale. Though the predictive accuracy of the research method improved using the BUB approach, the current research was more important, as an illustration, of how product feature surveys can impact market shares earlier in a product's life cycle than is possible now with most forms of conjoint. Where brand managers can single out one or two popular features that the public is not aware of, marketing messages can increase sales without a product re-design. In the case of traditional conjoint surveys, brand managers are limited to predicting changes in market share that would result from a new combination of features, rather than changing beliefs about existing products. If a manager finds that his/her product has a popular feature that consumers are unaware of, that feature becomes the advertising focus.

Multi-layer Classification and Segmentation using Ensemble Learning for Opinion Mining

In recent years, social media has become the prime place for advertisements, activities, campaigns, protests etc. It provides a platform for the people to express their views and beliefs to the masses. The user beliefs, practices and interests are of great importance to organizations and provide insight into the minds of users. Data Mining is one such tool that enables these organizations to extract relevant information from user data, which can be analyzed to create a knowledge set and determine user opinion that allows companies to create products tailored to the user. Data Mining of Twitter and other social platforms is of a great importance because, its large user base is a goldmine of public opinions and views which if analyzed properly, can potentially be used to predict campaign results and product assessments and likeability. This project proposes a classification scheme that aims to perform Segmentation based Twitter Opinion Mining using Ensemble Learning. The proposed scheme is able to detect and filter out bots and uses text segmentation for effective text classification and part of speech tagging.

A Hidden Markov Framework to Capture Human–Machine Interaction in Automated Vehicles

ABSTRACTA Hidden Markov Model framework is introduced to formalize the beliefs that humans may have about the mode in which a semi-automated vehicle is operating. Previous research has identified various “levels of automation,” which serve to clarify the different degrees of a vehicle’s automation capabilities and expected operator involvement. However, a vehicle that is designed to perform at a certain level of automation can actually operate across different modes of automation within its designated level, and its operational mode might also change over time. Confusion can arise when the user fails to understand the mode of automation that is in operation at any given time, and this potential for confusion is not captured in models that simply identify levels of automation. In contrast, the Hidden Markov Model framework provides a systematic and formal specification of mode confusion due to incorrect user beliefs. The framework aligns with theory and practice in various interdisciplinary approaches to the field of vehicle automation. Therefore, it contributes to the principled design and evaluation of automated systems and future transportation systems.

A Constrained Randomization Approach to Interactive Visual Data Exploration with Subjective Feedback

Data visualization and iterative/interactive data mining are growing rapidly in attention, both in research as well as in industry. However, while there are a plethora of advanced data mining methods and lots of works in the field of visualization, integrated methods that combine advanced visualization and/or interaction with data mining techniques in a principled way are rare. We present a framework based on constrained randomization which lets users explore high-dimensional data via ‘subjectively informative’ two-dimensional data visualizations. The user is presented with ‘interesting’ projections, allowing users to express their observations using visual interactions that update a background model representing the user's belief state. This background model is then considered by a projection-finding algorithm employing data randomization to compute a new ‘interesting’ projection. By providing users with information that contrasts with the background model, we maximize the chance that the user encounters striking new information present in the data. This process can be iterated until the user runs out of time or until the difference between the randomized and the real data is insignificant. We present two case studies, one controlled study on synthetic data and another on census data, using the proof-of-concept tool SIDE that demonstrates the presented framework.