NEVER CHANGE A RUNNING SYSTEM? HOW STATUS QUO-THINKING CAN INHIBIT SOFTWARE AS A SERVICE ADOPTION IN ORGANIZATIONS

Despite the “buzz” about Software as a Service (SaaS), decision makers still often refrain from replacing their existing in-house technologies with innovative IT services. Industry reports indicate that the skeptical attitude of decision makers stems primarily from a high degree of uncertainty that exists, for example, due to insufficient experience with the new technology, a lack of best practice approaches, and missing lighthouse projects. Whereas previous research is predominantly focused on the advantages of SaaS, behavioral economics conclusively demonstrate that reference points like the evaluation of the incumbent technology or a familiar product are oftentimes prevalent when decisions are made under uncertainty. In this context, Status Quo-Thinking may inhibit decisions in favor of potentially advantageous IT service innovations. Drawing on Prospect Theory and Status Quo Bias re-search, we derive and empirically test a research model that explicates the influence of the incumbent technology on the evaluation of SaaS. Based on a large-scale empirical study, we demonstrate that the decision makers’ attitude toward SaaS is highly dependent on their current systems and their level of SaaS. A lack of SaaS experience will increase the impact of the Status Quo, thus inhibiting a potential advantageous adoption of the new technology

PATIENTS’ ACCEPTANCE AND RESISTANCE TOWARD THE HEALTH CLOUD: AN INTEGRATION OF TECHNOLOGY ACCEPTANCE AND STATUS QUO BIAS PERSPECTIVES

The latest technological trends such as health cloud provide a strong infrastructure and offer a true enabler for healthcare services over the Internet. Despite its great potential, there are gaps in our understanding of how users evaluate change related to the health cloud and decide to resist it. According to the technology acceptance and status quo bias perspectives, this study develops an integrated model to explain patients’ intention to use and resistance to health cloud services. A field survey was conducted in Taiwan to collect data from patients. The structural equation model was used to examine the data. The results showed that patient resistance to use was caused by inertia, perceived value, and transition costs. Perceived usefulness (PU) and perceived ease of use (PEOU) have positive and direct effects on behavioral intention to use, and PEOU appears to have a positive direct effect on PU. The results also indicated that the relationship between intention to use and resistance to use had a significant negative effect. Our study illustrates the importance of incorporating user resistance in technology acceptance studies in general and health technology usage studies in particular, grounds for a resistance model of resistance that can serve as the starting point for future research in this relatively unexplored yet potentially fertile area of research

Status Quo Bias in Users’ Information Systems (IS) Adoption and Continuance Intentions: A Literature Review and Framework

Information systems (IS) adoption and continuance intentions of users have a dominant effect on digital transformation in organisations. However, organisations undergoing digital transformation face substantial barriers due to user resistance to IS implementations. Status quo bias (SQB) plays a vital role in users’ decision-making regarding adopting new IS or continuing to use existing IS. Despite recent research to validate the effects of SQB on user resistance to IS implementations, how SQB affects the IS adoption and continuance intentions of users remain poorly understood, making it harder to develop ways of successfully dealing with it. To address the gap, we performed a systematic literature review on SQB in IS research. Our proposed framework incorporates the psychological phenomena promoting the status quo, SQB theory constructs, levels of SQB influence, and factors reducing the user resistance to IS implementations to enhance the understanding of IS adoption and continuance intentions

Explaining Physicians’ Acceptance and Resistance to the NHI Pharmacloud: A Theoretical Model and Empirical Test

The PharmaCloud allows physicians to streamline many of their healthcare processes and ensure patient safety in a more efficient and cost-effective manner. Despite its great potential, however, there are gaps in our understanding of how physicians evaluate change in relation to the PharmaCloud and why they decide to resist it. Thus, this study develops an integrated model to explain physicians’ intention to use the PharmaCloud and their intention to resist it. A field survey was conducted in Taiwan to collect data from physicians. Structural equation modeling (SEM) using the partial least squares (PLS) method was employed to test the research model. The results show that physicians’ resistance to the use of the PharmaCloud is the result of regret avoidance, inertia, perceived value, transition costs, and perceived threat. Information quality, system quality, and service quality are shown to have positive and direct effects on physicians’ intention to use the PharmaCloud. Our study illustrates the importance of incorporating user resistance in technology acceptance studies in general and health technology usage studies in particular, providing grounds for a model of resistance that can serve as the starting point for future research in this relatively unexplored yet potentially fertile area of research

Resistance of multiple stakeholders to e-health innovations: Integration of fundamental insights and guiding research paths

Consumer/user resistance is considered a key factor responsible for the failure of digital innovations. Yet, existing scholarship has not given it due attention while examining user responses to e-health innovations. The present study addressed this need by consolidating the existing findings to provide a platform to motivate future research. We used a systematic literature review (SLR) approach to identify and analyze the relevant literature. To execute the SLR, we first specified a stringent search protocol with specific inclusion and exclusion criteria to identify relevant studies. Thereafter, we undertook an in-depth analysis of 72 congruent studies, thus presenting a comprehensive structure of findings, gaps, and opportunities for future research. Specifically, we mapped the relevant literature to elucidate the nature and causes of resistance offered by three key constituent groups of the healthcare ecosystem—patients, healthcare organizational actors, and other stakeholders. Finally, based on the understanding acquired through our critical synthesis, we formulated a conceptual framework, classifying user resistance into micro, meso, and macro barriers which provide context to the interventions and strategies required to counter resistance and motivate adoption, continued usage, and positive recommendation intent. Being the first SLR in the area to present a multi-stakeholder perspective, our study offers fine-grained insights for hospital management, policymakers, and community leaders to develop an effective plan of action to overcome barriers that impede the diffusion of e-health innovations.publishedVersionPaid open acces

Fatores que afetam a adoção de análises de Big Data em empresas

With the total quantity of data doubling every two years, the low price of computing and data storage, make Big Data analytics (BDA) adoption desirable for companies, as a tool to get competitive advantage. Given the availability of free software, why have some companies failed to adopt these techniques? To answer this question, we extend the unified theory of technology adoption and use of technology model (UTAUT) adapted for the BDA context, adding two variables: resistance to use and perceived risk. We used the level of implementation of these techniques to divide companies into users and non-users of BDA. The structural models were evaluated by partial least squares (PLS). The results show the importance of good infrastructure exceeds the difficulties companies face in implementing it. While companies planning to use Big Data expect strong results, current users are more skeptical about its performance.Con la cantidad total de datos duplicándose cada dos años, el bajo precio de la informática y del almacenamiento de datos, la adopción del análisis Big Data (BDA) es altamente deseable para las empresas, como un instrumento para conseguir una ventaja competitiva. Dada la disponibilidad de software libre, ¿por qué algunas empresas no han adoptado estas técnicas? Para responder a esta pregunta, ampliamos la teoría unificada de la adopción y uso de tecnología (UTAUT) adaptado para el contexto BDA, agregando dos variables: resistencia al uso y riesgo percibido. Utilizamos el grado de implantación de estas técnicas para dividir las empresas entre: usuarias y no usuarias de BDA. Los modelos estructurales fueron evaluados con partial least squres (PLS). Los resultados muestran que la importancia de una buena infraestructura excede las dificultades que enfrentan las empresas para implementarla. Mientras que las compañías que planean usar BDA esperan muy buenos resultados, las usuarias actuales son más escépticos sobre su rendimiento.Com a quantidade total de dados duplicando a cada dois anos, o baixo preço da computação e do armazenamento de dados tornam a adoção de análises de Big Data (BDA) desejável para as empresas, como aquelas que obterão uma vantagem competitiva. Dada a disponibilidade de software livre, por que algumas empresas não adotaram essas técnicas? Para responder a essa pergunta, estendemos a teoria unificada de adoção e uso de tecnologia (UTAUT) adaptado para o contexto do BDA, adicionando duas variáveis: resistência ao uso e risco percebido. Usamos a nível da implementação da tecnologia para dividir as empresas em usuários e não usuários de técnicas de BDA. Os modelos estruturais foram avaliados por partial least squares (PLS). Os resultados mostram que a importância de uma boa infraestrutura excede as dificuldades que as empresas enfrentam para implementá-la. Enquanto as empresas que planejam usar Big Data esperam resultados fortes, os usuários atuais são mais céticos em relação ao seu desempenho

Understanding User’s Switching Intention on Mobile Payment Platforms

With the development of mobile payment (m-payment) service, the competition accordingly increases among m-payment market. Users face multiple choices when adopting m-payment services. It is critical for both scholars and m-payments providers to understand what the underlying factors can influence user’s switching from one incumbent m-payment platform to another. To solve this question, we employ a push-pull-mooring (PPM) framework to build the research model. We propose that user’s dissatisfaction on incumbent m-payment provider is the main push factor for user’s switching. The attractiveness of alternative and peer influence are the pull factors influencing user’s switching. Cognitive lock-in, as the mooring factor, could influence switching intention directly. Additionally, we posit that cognitive lock-in can moderate the effects of both push and pull factors on user’s switching intention. This study will use survey methodology and structural equation modelling approach to test the hypotheses

UNDERSTANDING THE BENEFIT STRUCTURE OF CLOUD STORAGE AS A MEANS OF PERSONAL ARCHIVING - A CHOICE-BASED CONJOINT ANALYSIS

While cloud storage has seen an increasing rise in demand and diffusion, it is also becoming a commodity, which makes it more difficult for cloud storage providers to be competitive in the market. To be successful as a storage provider, it is crucial to understand customer preferences so that these can be addressed accordingly. In this paper, we investigate consumer cloud storage choice decisions by employing a conjoint analysis that is based on empirical data collected from 340 participants and analyzed by means of hierarchical Bayes estimation. Our findings indicate significant differences in consumer preferences for price, storage capacity, encryption mechanism and accessibility. Based on these differences, we derive three consumer clusters that also exhibit differences in, e.g., their privacy concerns and risk beliefs. Based on our findings, we highlight some practical implications that can aid cloud storage providers in service design and adjustment decisions. This study contributes to the literature by providing a better understanding of the benefit structure and trade-offs user make in the choice of storage services. As an alternative to commercial conjoint software packages, we further contribute a method that can be adopted by other scholars who seek to conduct conjoint analyses using free software