•A firm's competence in maintaining quality (i.e., consistency and completeness) of corporate data positively affects the firm's adoption intention for big data analytics.•A firm's positive ...experience (i.e., benefit perceptions) in utilizing external source data could encourage its adoption intention for big data analytics.•A firm's positive experience (i.e., benefit perceptions) in utilizing internal source data could hamper its adoption intention for big data analytics.
Big data analytics associated with database searching, mining, and analysis can be seen as an innovative IT capability that can improve firm performance. Even though some leading companies are actively adopting big data analytics to strengthen market competition and to open up new business opportunities, many firms are still in the early stage of the adoption curve due to lack of understanding of and experience with big data. Hence, it is interesting and timely to understand issues relevant to big data adoption. In this study, a research model is proposed to explain the acquisition intention of big data analytics mainly from the theoretical perspectives of data quality management and data usage experience. Our empirical investigation reveals that a firm's intention for big data analytics can be positively affected by its competence in maintaining the quality of corporate data. Moreover, a firm's favorable experience (i.e., benefit perceptions) in utilizing external source data could encourage future acquisition of big data analytics. Surprisingly, a firm's favorable experience (i.e., benefit perceptions) in utilizing internal source data could hamper its adoption intention for big data analytics.
Stock price prediction has long been the subject of research because of the importance of accuracy of prediction and the difficulty in forecasting. Traditionally, forecasting has involved linear ...models such as AR and MR or nonlinear models such as ANNs using standardized numerical data such as corporate financial data and stock price data. Due to the difficulty of securing a sufficient variety of data, researchers have recently begun using convolutional neural networks (CNNs) with stock price graph images only. However, we know little about which characteristics of stock charts affect the accuracy of predictions and to what extent. The purpose of this study is to analyze the effects of stock chart characteristics on stock price prediction via CNNs. To this end, we define the image characteristics of stock charts and identify significant differences in prediction performance for each characteristic. The results reveal that the accuracy of prediction is improved by utilizing solid lines, color, and a single image without axis marks. Based on these findings, we describe the implications of making predictions only with images, which are unstructured data, without using large amounts of standardized data. Finally, we identify issues for future research.
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Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Since reusable launch vehicles have revolutionized access to space, space tourism has received enormous policy and research attention. However, such growth is occurring within a wider context of ...concerns over climate change, emissions, and space debris. Although the space industries have enormous environmental impacts, few studies have been undertaken on the sustainability of space tourism. Therefore, we aim to create and assess an extended value-beliefs-norms theory with environmental, social, and governance (ESG) factors, trust in artificial intelligence (AI), and the benefits of AI, in comparing three types of space tourism (Earth, suborbital, and orbital). To achieve the goals, multi-method analyses of 1,000 respondents were applied, including partial least squares-structural equation modeling, multi-group analysis, fuzzy-set Qualitative Comparative Analysis, and deep learning. Results revealed that the extended value-belief-norm model well explains space tourist behavior, ESG also has significant roles on the research model, and the three types have unique characteristics.
Particularly in the healthcare service domain, social robots are expected to be good assistants, advisers, or practitioners. To increase the effectiveness of healthcare services provided by social ...robots, patients must comply with their requests. Research is plentiful on what makes patients comply with healthcare advice. In this paper, which is based on Bulgurcu’s study of rationality-based beliefs, command-compliance theory, and social exchange theory, we propose a research model of compliance during interaction with social robots, examining beliefs about and overall assessments of the consequences of complying with robot requests and extending the findings of previous studies to the setting of healthcare services. We specifically investigate the perceived level of politeness in robots’ speech and gestures as a determinant of compliance intention. Using a social robot, NAO, as a provider of healthcare services, we conducted an experiment. The results suggest that the aforementioned theories are useful in understanding user behaviors toward social robots in a healthcare service setting. Interestingly, and unlike in other settings, the perceived level of politeness of a social robot in a healthcare service setting negatively affects the perceived benefit of compliance, and, hence, intention to comply. A lower politeness level is closer to a command or strong recommendation than a suggestion or causal recommendation, which is common in shopping, tourism, or convention settings. The findings of this study imply that polite behavior from a social robot is an important factor in the compliance of healthcare service users. Direct speech with polite gestures is the most effective way to increase patient compliance in with healthcare advice provided by social robots in healthcare settings. However, higher levels of politeness do not always increase patients’ intention to comply.
•This study compared technology acceptance theories in terms of AI-based intelligent products.•VAM performed best in modeling for understanding the acceptance of AI-based intelligent ...products.•Enjoyment is a crucial factor in terms of AI-based intelligent products, unlike other products.•First applied decomposition analysis in IS research for quantifying the influence among factors.•Users considered the characteristics of combined products rather than the AI technology itself.
The rapid growth of artificial intelligence (AI) technology has prompted the development of AI-based intelligent products. Accordingly, various technology acceptance theories have been used to explain acceptance of these products. This comparative study determines which models best explain consumer acceptance of AI-based intelligent products and which factors have the greatest impact in terms of purchase intention. We assessed the utility of the Technology Acceptance Model (TAM), the Theory of Planned Behavior (TPB), the Unified Theory of Acceptance and Use of Technology (UTAUT), and the Value-based Adoption Model (VAM) using data collected from a survey sample of 378 respondents, modeling user acceptance in terms of behavioral intention to use AI-based intelligent products. In addition, we employed decomposition analysis to compare each factor included in these models in terms of influence on purchase intention. We found that the VAM performed best in modeling user acceptance. Among the various factors, enjoyment was found to influence user purchase intention the most, followed by subjective norms. The findings of this study confirm that acceptance of highly innovative products with minimal practical value, such as AI-based intelligent products, is more influenced by interest in technology than in utilitarian aspects.
Making effective decisions often depends on having the right information at the right time. Decision support systems were originally designed to help decision makers by automating some of the ...decision process. Today's decision support systems should take this a step further: making decisions proactively and intelligently by automatically detecting users' contextual data. Context-aware technology-based applications currently only provide limited personalization services that reference the user's context and preferences; these systems do not fully make use of sophisticated decision making capabilities. Hence, this paper describes how decision making and context-aware computing are jointly used to establish context-aware intelligent decision support systems. To incorporate these capabilities, we address a framework of context-aware intelligent intelligent decision support systems (CAIDSS), with the description of the subsystems within.
The purpose of this study is to provide an overview of extant research regarding XR technology and its effect on consumer wellbeing. With the hopes of informing marketing practitioners on XR consumer ...psychology, in preparation for the Metaverse.
To achieve the above aim, two types of analysis took place. Firstly, a bibliometric analysis was conducted which was then followed by a framework-based structured literature review. The latter entailed an analysis of 81 articles evaluated from a positive psychological approach.
Following the TCCM framework, the analysis revealed the most common psychological theories demonstrating potential avenues for XR to impact consumer wellbeing. Moreover, researchers found preliminary links between, theory, characteristics, and contexts. Giving a preliminary description of how theory manifests into reality. Finally, the overview of extant literature was used to propose new avenues for future research pertaining to marketing, the Metaverse, and consumer effects.
In conclusion, the paper provides stakeholder insights which can ensure minimal consumer risk and sustainable use of the XR technology and Metaverse. While addressing the need for more research that uncovers the psychological effects of emerging technologies, so to prepare for the Metaverse. This is especially important when considering the current upsurge of these technologies and the uncertainties associated with their novelty and the idea of an 'always on' consumer.
•We propose a model-based method of public health monitoring for solitary elderly people.•The method generates secondary situational information from activity data gathered at home.•An experiment was ...performed with 1236 actual samples.•The proposed method contributes to improve the quality of health monitoring in terms of accuracy and finding outliers.
IT vendors routinely use social media such as YouTube not only to disseminate their IT product information, but also to acquire customer input efficiently as part of their market research strategies. Customer responses that appear in social media, however, are typically unstructured; thus, a fairly large data set is needed for meaningful analysis. Although identifying customers’ value structures and attitudes may be useful for developing targeted or niche markets, the unstructured and volume-heavy nature of customer data prohibits efficient and economical extraction of such information. Automatic extraction of customer information would be valuable in determining value structure and strength. This paper proposes an intelligent method of estimating causality between user profiles, value structures, and attitudes based on the replies and published content managed by open social network systems such as YouTube. To show the feasibility of the idea proposed in this paper, information richness and agility are used as underlying concepts to create performance measures based on media/information richness theory. The resulting deep sentiment analysis proves to be superior to legacy sentiment analysis tools for estimation of causality among the focal parameters.
To date, plenty of theories, such as the expectation–confirmation model (ECM), have been proposed to explain why and how consumers are motivated to continue to use web-based services. In particular, ...various affective factors have been proposed to explain user satisfaction and continued use of web-based services recently in the IS community. In IS continuance research, several affective factors, such as perceived playfulness, perceived enjoyment and pleasure, have been examined. Affective factors discussed in the existing continuance intention-related studies are mostly short-term emotional factors like this. However, if a user’s continued usage of a web-based service can be interpreted as a long-term relationship between a user and the service, then the factors such as familiarity and intimacy which are the emotions created accumulatively over time based on an established relationship with the user can be helpful for better explaining the user’s continuance intention. Also, if relationships between consumers and web-based services have been built up due to repetitive usage, then we can assume that both affective and cognitive factors may explain consumers’ continuance intention. Hence, the purpose of this paper is to propose an extended ECM. We focus on two new constructs, familiarity and intimacy, as persistent affective factors. To investigate how cognitive and affective factors are interrelated in continuance intention, we conducted surveys focusing on users’ continued intention to use web-based services. The results indicate that continuance intention is affected conjointly by cognitive factors, such as perceived usefulness, and affective factors, such as familiarity and intimacy. However, the effects of affective factors such as intimacy were larger than those of cognitive factors such as perceived usefulness. In addition, the results indicate that intimacy, a purer affective concept than familiarity, affects users’ continuance intention more than familiarity.
•How to resolve power consumption–service quality paradox is a critical issue.•Legacy systems (EECA, ESCA) do not fully consider the paradox.•A novel method of optimizing the sensing cycle and ...service time is proposed.•The negotiation mechanism finds an optimal level of personalization.•The negotiation mechanism avoids computing total cost of all levels of personalization.
Context-aware applications, which consist of a sensor system, a reasoning system and service artifacts such as mobile devices, kiosks and robots, require data from the sensors to be queried on a continuous basis. The smaller the sensing interval and the greater the amount of service time, the more accurate the service, but the more energy is consumed. Thus, use of context-aware applications always involves a trade-off. In this paper, we propose an automatic method of optimizing the level of personalization involving the sensing cycle and service time of a personalized application. The method proposes a quadratic form of total cost curve which demonstrated that the minimum identified value is always the global optimum. This eliminates the necessity of an exhaustive search for the minimum value for all levels of personalization.
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