Factors Influencing the Adoption of Smart-home Appliances: Users\u27 Perspectives

Smart appliances will be a significant part of the annual $300 billion smart-home market by 2025. Their use is expected to grow at a compounded annual growth rate of 31% for the foreseeable future. Currently, 12-16% of households use smart-home products in the U.S., including thermostats, TVs, refrigerators, coffee machines, garage door openers, and vision-equipped doorbells. Smart appliances provide significant benefits to us over traditional appliances. Smart appliances simplify our lives by automating various tasks in our homes and allowing us to monitor and control them remotely from our offices, grocery stores, and wherever we may be. Despite the usefulness and popularity of some smart appliances, recent research has shown that their adoption rate may not be increasing as expected. Every day, manufacturers rush to make appliances smarter through increased automation and connectivity without paying attention to consumers\u27 concerns about their use. However, if the manufacturers do not address consumers\u27 concerns about their smart appliances, they may not readily be adopted solely based on their features. Scholars have explored technology adoption through various sociology, psychology, and information science theories. The universal theory of acceptance and use of technology (UTAUT) combines these widely researched theories into a framework that can be used to explore the technology adoption process. This research qualitatively explores the critical factors antecedent to consumers\u27 adoption behavior of smart appliances using the UTAUT framework. The findings from this research have expanded the application of UTAUT to address the adoption of smart-home appliances. Further, to aid the adoption process, this research makes important suggestions to practitioners involved in developing, manufacturing, and marketing smart appliances: the need to focus on interoperability, the need to lower the consumers’ effort, and the need to handle consumers\u27 data ethically. Finally, the research also offers remedies to counteract consumers’ resistance to adopting smart appliances: providing an acceptable level of automation and connectivity in appliances

How do potential users perceive the adoption of new technologies within the field of Artificial Intelligence and Internet-of-Things? - A revision of the UTAUT 2 model using Voice Assistants

The following study investigates the perception potential users have when considering the adoption of voice assistants (VAs). VAs are considered to possess characteristics linkable to both, Artificial Intelligence (AI) and the Internet-of-Things (IoT). This thesis aims to provide a deeper understanding of the determinants influencing the adoption of the new VA technology using the Unified Theory of Acceptance and Use of Technology 2 model (UTAUT 2), a theoretical model explaining technology adoption and usage behaviour. The amount of gadgets being released to the market which possess characteristics of the AI and IoT technology increases constantly, while the 2012 version of the UTAUT 2 model was not constructed for these. In a qualitative approach conducting four focus groups, the aim of this study is to find out about the perceptions of potential future users on the VA technology and as a consequence amend the current UTAUT 2 model to fit newly upcoming technologies which possess similar characteristics as VAs within the AI and IoT field. The study found out that while hedonic motivation seems to be of inferior relevance, the determinants data security, compatibility and relationship with the device are essential influencing factors to take into consideration when trying to fully understand users’ technology adoption perceptions. However, the fact that these technologies are still in the early stage of adoption make it difficult for future users, to fully judge their own adoption behaviour if they are no members of the early innovation adoption curve stages. For further research, it is recommended to look into different sampling groups and apply the model resulting from this study to new upcoming technologies within the area of AI and IoT

Perceived creepiness in response to smart home assistants: A multi-method study

Smart home assistants (SHAs) have gained a foothold in many households. Although SHAs have many beneficial capabilities, they also have characteristics that are colloquially described as creepy – a fact that may deter potential users from adopting and utilizing them. Previous research has examined SHAs neither from the perspective of resistance nor the perspective of creepiness. The present research addresses this gap and adopts a multi-method research design with four sequential studies. Study 1 serves as a pre-study and provides initial exploratory insights into the concept of creepiness in the context of SHAs. Study 2 focuses on developing a measurement instrument to assess perceived creepiness. Study 3 uses an online experiment to test the nomological validity of the construct of creepiness in a larger conceptual model. Study 4 further elucidates the underlying behavioral dynamics using focus group analysis. The findings contribute to the literature on the dark side of smart technology by analyzing the triggers and mechanisms underlying perceived creepiness as a novel inhibitor to SHAs. In addition, this study provides actionable design recommendations that allow practitioners to mitigate end users’ potential perceptions of creepiness associated with SHAs and similar smart technologies

Hybrid jobs in the retail industry. Redesigning organizations, processes and work

Retail industry is changing, as a consequence of online competition, customers' behavior and technological advancements, and retail jobs are transforming as well, with a change of the skills required. The purpose of this thesis is to investigate how these jobs are changing and how they will likely become in some years

Revisiting Ralph Sprague’s Framework for Developing Decision Support Systems

Ralph H. Sprague Jr. was a leader in the MIS field and helped develop the conceptual foundation for decision support systems (DSS). In this paper, I pay homage to Sprague and his DSS contributions. I take a personal perspective based on my years of working with Sprague. I explore the history of DSS and its evolution. I also present and discuss Sprague’s DSS development framework with its dialog, data, and models (DDM) paradigm and characteristics. At its core, the development framework remains valid in today’s world of business intelligence and big data analytics. I present and discuss a contemporary reference architecture for business intelligence and analytics (BI/A) in the context of Sprague’s DSS development framework. The practice of decision support continues to evolve and can be described by a maturity model with DSS, enterprise data warehousing, real-time data warehousing, big data analytics, and the emerging cognitive as successive generations. I use a DSS perspective to describe and provide examples of what the forthcoming cognitive generation will bring

EDGE-CoT: next generation cloud computing and its impact on business

Purpose – The main objective of this paper is to analyze the potential impact of future cloud computing trends on business, from the perspective of specialists in the area. Design/ methodology/ approach - Qualitative approach that includes literature review and nine semi-structured interviews with proclaimed influencers and global thought leaders in cloud computing, highlighting Jeff Barr, Vice President of Amazon Web Services. Findings -5G networks will enable the emergence of the Edge-CoT architecture, that will consequently drive the increased application of Artificial Intelligence/ Machine Learning (AI/ML) and Robotics. The combination of Edge-CoT, Robotics and AI/ML triggers the development of Smart Cities and Industry 4.0. Simultaneously, Cloud alone will benefit of increased connectivity and will be the preferred business architecture comparing to EdgeCoT. New industries and businesses will result from the Edge-CoT, and the existing companies will benefit mainly from an improved customer experience. Major business challenges triggered by Edge-CoT include workforce re-skilling, promotion of the agile approach and a cultural shift towards risk-taking. Research limitations/implications - The research study was limited to the analysis of a selected set of cloud computing trends. Moreover, the data collection process was limited to 9 cloud experts, hindering a possible generalization. Originality/value – This study uses a qualitative approach to listen to market experts and cross with the theoretical findings to date, consequently bringing theory and practice closer together.Objetivo - O objetivo deste estudo consiste em analisar o potencial impacto das tendências futuras de cloud computing na gestão das empresas, a partir da visão de especialistas da área. Metodologia- Abordagem qualitativa que engloba revisão de literatura e nove entrevistas semiestruturadas com proclamados influencers e lideres globais em cloud computing, destacando-se Jeff Barr, o Vice-presidente da Amazon Web Services. Resultado - As redes 5G possibilitarão o surgimento da arquitetura Edge-CoT, que consequentemente impulsionará o aumento da aplicação de Inteligência Artificial (AI) e robótica. A combinação de Edge-CoT, Robótica e AI desencadeia o desenvolvimento de Smart Cities e Industry 4.0. Simultaneamente, a Cloud sozinha beneficiará do aumento da conectividade e será a arquitetura preferida comparativamente a Edge-CoT. Novos setores e negócios resultarão do Edge-CoT, e as empresas existentes beneficiarão principalmente de uma melhor experiência do cliente. Os principais desafios organizacionais desencadeados pelo Edge-CoT incluem a requalificação da força de trabalho, a adoção da abordagem agile e uma mudança cultural que estimule experimentos tecnológicos. Restrição da pesquisa - O processo de recolha de dados foi limitado a 9 especialistas em cloud computing, dificultando assim uma possível generalização. Originalidade/ Valor - Este estudo utiliza uma abordagem qualitativa para ouvir os especialistas do mercado e cruzar com os resultados teóricos até o momento, aproximando assim a teoria da prática

Creating business value from big data and business analytics : organizational, managerial and human resource implications

This paper reports on a research project, funded by the EPSRC’s NEMODE (New Economic Models in the Digital Economy, Network+) programme, explores how organizations create value from their increasingly Big Data and the challenges they face in doing so. Three case studies are reported of large organizations with a formal business analytics group and data volumes that can be considered to be ‘big’. The case organizations are MobCo, a mobile telecoms operator, MediaCo, a television broadcaster, and CityTrans, a provider of transport services to a major city. Analysis of the cases is structured around a framework in which data and value creation are mediated by the organization’s business analytics capability. This capability is then studied through a sociotechnical lens of organization/management, process, people, and technology. From the cases twenty key findings are identified. In the area of data and value creation these are: 1. Ensure data quality, 2. Build trust and permissions platforms, 3. Provide adequate anonymization, 4. Share value with data originators, 5. Create value through data partnerships, 6. Create public as well as private value, 7. Monitor and plan for changes in legislation and regulation. In organization and management: 8. Build a corporate analytics strategy, 9. Plan for organizational and cultural change, 10. Build deep domain knowledge, 11. Structure the analytics team carefully, 12. Partner with academic institutions, 13. Create an ethics approval process, 14. Make analytics projects agile, 15. Explore and exploit in analytics projects. In technology: 16. Use visualization as story-telling, 17. Be agnostic about technology while the landscape is uncertain (i.e., maintain a focus on value). In people and tools: 18. Data scientist personal attributes (curious, problem focused), 19. Data scientist as ‘bricoleur’, 20. Data scientist acquisition and retention through challenging work. With regards to what organizations should do if they want to create value from their data the paper further proposes: a model of the analytics eco-system that places the business analytics function in a broad organizational context; and a process model for analytics implementation together with a six-stage maturity model