Success Factors for the Acceptance of Smart Home Technology Concepts

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Digitalization changes consumer markets rapidly. There is an increasing focus on technological innovations as well as concepts that improve daily routines and offer support for self-determined living. Various technology companies have recognized this need and developed different types of hard- and software products, so-called Smart Home (SH) technology. In Germany, the SH technology market is still in its infancy. To increase market success, there is a need to understand which factors influence the acceptance of those products. In this study, the acceptance of different SH concepts was examined, using the frame- work of the Unified Theory of Acceptance and Use of Technology (UTAUT). In a field experiment with 496 participants, acceptance models for two differ- ent SH concepts were assessed. The results of the empirical study suggest that the UTAUT is a valid framework for modelling the acceptance of SH technol- ogy. Overall success factors for the acceptance are Performance Expectancy, Effort Expectancy, Social Influence, and Facilitating Conditions. While there was no difference in the overall structure of the UTAUT models for the different SH concepts, distinct strengths emerged. Theoretical as well as practical implications of these findings for the marketing of SH products are discussed.

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... It is defined as "the degree to which an individual believes that an organizational and technical infrastructure exists to support use of the system" (Venkatesh et al., 2003). This construct has been utilized in SH literature in several studies (Aldossari & Sidorova, 2020;Baudier et al., 2020;Salomon & Müller, 2019), by using almost the same measures, such as possessing the necessary resources and knowledge to use SHs, and to what extent SHs are compatible with existing households and whether there is a specific person or group available to provide help and support when needed. ...
... Effort Expectancy (EE) was originally defined as "the degree of ease associated with the use of the system" (Venkatesh et al., 2003) and reflects on three variables: perceived ease of use (TAM/TAM2) and complexity in Model of Personal Computing (PC) utilization (MPCU). Similar to FCs, this construct has been used in several SH studies (Aldossari & Sidorova, 2020;Baudier et al., 2020;Salomon & Müller, 2019). EE captures the ease of using SH technology, interacting with smart home devices and services, and how easy it is for users to become skillful at using SH devices, services and applications. ...
... This construct reflects on the following variables: PU, jobfit, relative advantage, extrinsic motivation, and outcome expectations. Consistent with EE and FCs, performance expectancy is used in the same references (Aldossari & Sidorova, 2020;Baudier et al., 2020;Salomon & Müller, 2019), indicating the usage of UTAUT to conduct the associated quantitative studies. Performance expectancy captures the usefulness of SH technology and devices, accomplishing home activities more quickly and easily, and increasing people's productivity to perform their daily home activities. ...
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This survey aims to provide a coherent and bibliometric overview of the theories and constructs employed in smart homes acceptance and adoption literature. To achieve the study aims, we conducted a systematic search for every article related to the SH concept, services and applications, user acceptance and adoption, and integrated IoT home appliances and devices, in 10 major library databases, namely, IEEE Digital Library, ACM Digital Library, Association for Information Systems (AIS), Elsevier, Emerald, Taylor and Francis, Wiley InterScience, Springer, Inderscience, and Hindawi. These databases contain literature focusing on smart home adoption using IoT technology. 40 research articles of journal and peer-reviewed conferences were found relating to our research objective, presented and distributed chronologically, by publisher, country, theory and model, key construct, and with full bibliometrics for each article. Additionally, this survey includes a word cloud and a taxonomy of the entire factors used to understand users' acceptance and adoption of smart homes in different contexts and applications. This study has many advantages in covering the current research gap in the literature and also the researchers identify theoretical and practical research implications, research limitations, and recommendations for improving the acceptance and usage of smart homes literature.
... Es zeigt sich aber, dass die soziale Einflussnahme in normativer Hinsicht erheblichen Einfluss auf die Smart-Home-Akzeptanz haben kann (vgl. Bao et al., 2014;Yang, Lee & Zo, 2017;Pal et al., 2018b;Salomon & Müller, 2019 in Kapitel ...
... Shih, 2013;Park et al., 2017;Hubert et al., 2019) und die technische Kompatibilität (Bao et al., 2014;Yang et al., 2017;Pal et al., 2018b) als relevante Adoptionshürden ab. Auch der soziale Einfluss (Bao et al., 2014;Yang et al., 2017;Pal et al., 2018b;Salomon & Müller, 2019) ist zu nennen, was von den Autoren zum Teil auf das relativ hohe Alter der Befragten (Pal et al., 2018b), die relativ geringe Verbreitung von Smart Home (Yang et al., 2017) oder landesspezifische kulturelle Besonderheiten zurückgeführt wird (vgl. Bao et al., 2014). ...
Die bis dato eher verhaltene Nachfrage nach Smart-Home-Systemen gibt Anlass zu der Frage nach den maßgeblichen Adoptionsbarrieren. In Anbetracht des potentiellen Nutzens dieser Technologien für das Leben der Bewohner – besonders auch im fortschreitenden Alter – besteht darüber hinaus Forschungsbedarf, ob und in welcher Art sich das Alter(n) auf die Technologieakzeptanz auswirkt. Insbesondere sind auf subjektiver Wahrnehmung basierende Alterskonstrukte bisher kaum im Zusammenhang mit der Technologieadoption untersucht worden. Die Arbeit befasst sich mit den Determinanten der Akzeptanz von Smart-Home-Anwendungen seitens potentieller Übernehmer anhand populärer Theorien und Modelle zum individuellen Adoptionsverhalten und mit Blick auf die Einflusswirkung des chronologischen Alters bzw. des subjektiv empfundenen Alterns. Das gewählte Untersuchungsmodell basiert auf dem in der wissenschaftlichen Forschung weit verbreiteten und empirisch bewährten Technology Acceptance Model (Davis, Bagozzi & Warshaw, 1989). Zusätzlich werden alternativ externe Variablen in Form des chronologischen Alters bzw. der Future Time Perspective (Carstensen & Lang, 1996) einbezogen, um insbesondere die Wirkung auf den wahrgenommenen Nutzwert und die empfundene Einfachheit der Nutzung von Hausautomatisierung zu untersuchen. Betrachtetet werden zudem Mediator- und Moderatoreffekte hinsichtlich der nachgelagerten Einstellungsbildung und Übernahmeintention. Die Ergebnisse stützen die Hypothese, dass die subjektive Future Time Perspective (mit Blick auf die im Leben verbleibenden Möglichkeiten) einen größeren Erklärungsbeitrag zur individuellen Adoption von Smart Home-Systemen leisten kann als das chronologische Alter. The currently rather restrained demand for smart home systems raises the question of relevant adoption barriers. Given the technology’s potential benefits for residents’ lives, particularly with advancing age, there is a need to understand how age(ing) affects the acceptance of technology. Especially perceived age constructs have hardly been studied in technology adoption. The thesis investigates the determinants of smart home technology acceptance by potential adopters using popular theories and models of individual adoption behavior and moreover paying particular attention to the influence of chronological and perceived age. The research model is based on the widely used and empirically validated Technology Acceptance Model (Davis, Bagozzi & Warshaw, 1989), but integrates additional external variables to explore how chronological age and the Future Time Perspective (Carstensen & Lang, 1996) affect determining factors of home automatization acceptance. The effect on perceived usefulness and perceived ease of use is considered as well as mediator- and moderator-effects regarding downstream attitude formation and behavioral intention. The results support the hypothesis that the subjective future time perspective (focusing on opportunities remaining in life) has greater explanatory power for the individual adoption of smart home systems than chronological age.
Nowadays, the Internet of Things (IoT) and artificial intelligence (AI) is the emerging field in which researchers are still finding new methods and techniques to reduce human efforts. This chapter contains the basic introduction of the AI and IoT systems. The seven-layer architecture of the IoT frameworks is discussed with the functioning of the individual layers. The elementary facility of things and complete operation of the particular layer. In this chapter, we have discussed the relationship between AI and the IoT. There are various real-time applications of IoT with AI. Some of the uses of AI and IoT are also being discussed in the chapter.
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In technological era of rapid development in the fields of Internet of Things (IoT) and artificial intelligence (AI), there is a continuous event constituting a new stage in the changing world. AI has enabled devices to act smart and intelligent so that it brings actuation to the life of all by making it a reality. AI has widespread its wings with IoT. A number of data sets and machine learning algorithms are available to test the advancements and evaluate the integration of AI and IoT as Internet of Intelligent Things (IoIT). This chapter on the whole focuses on the major impact of recent impacts of IoT connected and communicating with a close relation with AI and bringing the solution which brings universally acceptable and welfare for all. In this chapter, we have included the random forest regression model of machine learning and evaluation of our model is done with variance score. It checks how they consumers and products communicate with each other and with humans through the Internet and make the store to act smart. This chapter focuses on the areas how the IoT and AI will interact together to make the devices IoIT, that is, intelligent devices to interact with the environment and produce smart results for the devices.
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Background Smart-home technologies, comprising environmental sensors, wearables and video are attracting interest in home healthcare delivery. Development of such technology is usually justified on the basis of the technology’s potential to increase the autonomy of people living with long-term conditions. Studies of the ethics of smart-homes raise concerns about privacy, consent, social isolation and equity of access. Few studies have investigated the ethical perspectives of smart-home engineers themselves. By exploring the views of engineering researchers in a large smart-home project, we sought to contribute to dialogue between ethics and the engineering community. Methods Either face-to-face or using Skype, we conducted in-depth qualitative interviews with 20 early- and mid-career smart-home researchers from a multi-centre smart-home project, who were asked to describe their own experience and to reflect more broadly about ethical considerations that relate to smart-home design. With participants’ consent, interviews were audio-recorded, transcribed and analysed using a thematic approach. ResultsTwo overarching themes emerged: in ‘Privacy’, researchers indicated that they paid close attention to negative consequences of potential unauthorised information sharing in their current work. However, when discussing broader issues in smart-home design beyond the confines of their immediate project, researchers considered physical privacy to a lesser extent, even though physical privacy may manifest in emotive concerns about being watched or monitored. In ‘Choice’, researchers indicated they often saw provision of choice to end-users as a solution to ethical dilemmas. While researchers indicated that choices of end-users may need to be restricted for technological reasons, ethical standpoints that restrict choice were usually assumed and embedded in design. Conclusions The tractability of informational privacy may explain the greater attention that is paid to it. However, concerns about physical privacy may reduce acceptability of smart-home technologies to future end-users. While attention to choice suggests links with privacy, this may misidentify the sources of privacy and risk unjustly burdening end-users with problems that they cannot resolve. Separating considerations of choice and privacy may result in more satisfactory treatment of both. Finally, through our engagement with researchers as participants this study demonstrates the relevance of (bio)ethics as a critical partner to smart-home engineering.
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Purpose: We present partial least squares (PLS) as an evolving approach to structural equation modeling (SEM), highlight its advantages and limitations and provide an overview of recent research on the method across various fields. Design/methodology/approach: In this review article we merge literatures from the marketing, management, and management information systems fields to present the state-of-the art of PLS-SEM research. Furthermore, we meta-analyze recent review studies to shed light on popular reasons for PLS-SEM usage. Findings: PLS-SEM has experienced increasing dissemination in a variety of fields in recent years with non-normal data, small sample sizes and the use of formative indicators being the most prominent reasons for its application. Recent methodological research has extended PLS-SEM’s methodological toolbox to accommodate more complex model structures or handle data inadequacies such as heterogeneity. Research limitations/implications: While research on the PLS-SEM method has gained momentum during the last decade, there are ample research opportunities on subjects such as mediation or multigroup analysis, which warrant further attention. Originality/value: This article provides an introduction to PLS-SEM for researchers that have not yet been exposed to the method. The article is the first to meta-analyze reasons for PLS-SEM usage across the marketing, management, and management information systems fields. Our cross-disciplinary review of recent research on the PLS-SEM method also makes this article useful for researchers interested in advanced concepts.
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Information technology (IT) acceptance research has yielded many competing models, each with different sets of acceptance determinants. In this paper, we (1) review user acceptance literature and discuss eight prominent models, (2) empiri- cally compare the eight models and their exten- sions, (3) formulate a unified model that integrates elements across the eight models, and (4) empiri- cally validate the unified model. The eight models reviewed are the theory of reasoned action, the technology acceptance model, the motivational model, the theory of planned behavior, a model combining the technology acceptance model and the theory of planned behavior, the model of PC utilization, the innovation diffusion theory, and the social cognitive theory. Using data from four organizations over a six-month period with three points of measurement, the eight models ex- plained between 17 percent and 53 percent of the variance in user intentions to use information technology. Next, a unified model, called the Unified Theory of Acceptance and Use of Tech- nology (UTAUT), was formulated, with four core determinants of intention and usage, and up to four moderators of key relationships. UTAUT was then tested using the original data and found to outperform the eight individual models (adjusted
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In this essay, we argue that pervasive digitization gives birth to a new type of product architecture: the layered modular architecture. The layered modular architecture extends the modular architecture of physical products by incorporating four loosely coupled layers of devices, networks, services, and contents created by digital technology. We posit that this new architecture instigates profound changes in the ways that firms organize for innovation in the future. We develop (1) a conceptual framework to describe the emerging organizing logic of digital innovation and (2) an information systems research agenda for digital strategy and the creation and management of corporate information technology infrastructures.
Advances in causal modeling techniques have made it possible for researchers to simultaneously examine theory and measures. However, researchers must use these new techniques appropriately. In addition to dealing with the methodological concerns associated with more traditional methods of analysis, researchers using causal modeling approaches must understand their underlying assumptions and limitations.
Getting an innovation adopted is difficult; a common problem is increasing the rate of its diffusion. Diffusion is the communication of an innovation through certain channels over time among members of a social system. It is a communication whose messages are concerned with new ideas; it is a process where participants create and share information to achieve a mutual understanding. Initial chapters of the book discuss the history of diffusion research, some major criticisms of diffusion research, and the meta-research procedures used in the book. This text is the third edition of this well-respected work. The first edition was published in 1962, and the fifth edition in 2003. The book's theoretical framework relies on the concepts of information and uncertainty. Uncertainty is the degree to which alternatives are perceived with respect to an event and the relative probabilities of these alternatives; uncertainty implies a lack of predictability and motivates an individual to seek information. A technological innovation embodies information, thus reducing uncertainty. Information affects uncertainty in a situation where a choice exists among alternatives; information about a technological innovation can be software information or innovation-evaluation information. An innovation is an idea, practice, or object that is perceived as new by an individual or an other unit of adoption; innovation presents an individual or organization with a new alternative(s) or new means of solving problems. Whether new alternatives are superior is not precisely known by problem solvers. Thus people seek new information. Information about new ideas is exchanged through a process of convergence involving interpersonal networks. Thus, diffusion of innovations is a social process that communicates perceived information about a new idea; it produces an alteration in the structure and function of a social system, producing social consequences. Diffusion has four elements: (1) an innovation that is perceived as new, (2) communication channels, (3) time, and (4) a social system (members jointly solving to accomplish a common goal). Diffusion systems can be centralized or decentralized. The innovation-development process has five steps passing from recognition of a need, through R&D, commercialization, diffusions and adoption, to consequences. Time enters the diffusion process in three ways: (1) innovation-decision process, (2) innovativeness, and (3) rate of the innovation's adoption. The innovation-decision process is an information-seeking and information-processing activity that motivates an individual to reduce uncertainty about the (dis)advantages of the innovation. There are five steps in the process: (1) knowledge for an adoption/rejection/implementation decision; (2) persuasion to form an attitude, (3) decision, (4) implementation, and (5) confirmation (reinforcement or rejection). Innovations can also be re-invented (changed or modified) by the user. The innovation-decision period is the time required to pass through the innovation-decision process. Rates of adoption of an innovation depend on (and can be predicted by) how its characteristics are perceived in terms of relative advantage, compatibility, complexity, trialability, and observability. The diffusion effect is the increasing, cumulative pressure from interpersonal networks to adopt (or reject) an innovation. Overadoption is an innovation's adoption when experts suggest its rejection. Diffusion networks convey innovation-evaluation information to decrease uncertainty about an idea's use. The heart of the diffusion process is the modeling and imitation by potential adopters of their network partners who have adopted already. Change agents influence innovation decisions in a direction deemed desirable. Opinion leadership is the degree individuals influence others' attitudes
Advances in causal modeling techniques have made it possible for researchers to simultaneously examine theory and measures. However, researchers must use these new techniques appropriately. In addition to dealing with the methodological concerns associated with more traditional methods of analysis, researchers using causal modeling approaches must understand their underlying assumptions and limitations.Most researchers are well equipped with a basic understanding of LISREL-type models. In contrast, current familiarity with PLS in the strategic management area is low. The current paper reviews four recent studies in the strategic management area which use PLS. The review notes that the technique has been applied inconsistently, and at times inappropriately, and suggests standards for evaluating future PLS applications. Copyright © 1999 John Wiley & Sons, Ltd.
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