Manipulating user resistance to large-scale information systems through influence tactics

How do medical professionals make sense (or not) of AI? A social-media-based computational grounded theory study and an online survey

To investigate opinions and attitudes of medical professionals towards adopting AI-enabled healthcare technologies in their daily business, we used a mixed-methods approach. Study 1 employed a qualitative computational grounded theory approach analyzing 181 Reddit threads in the several subreddits of r/medicine. By utilizing an unsupervised machine learning clustering method, we identified three key themes: (1) consequences of AI, (2) physician-AI relationship, and (3) a proposed way forward. In particular Reddit posts related to the first two themes indicated that the medical professionals' fear of being replaced by AI and skepticism toward AI played a major role in the argumentations. Moreover, the results suggest that this fear is driven by little or moderate knowledge about AI. Posts related to the third theme focused on factual discussions about how AI and medicine have to be designed to become broadly adopted in health care. Study 2 quantitatively examined the relationship between the fear of AI, knowledge about AI, and medical professionals' intention to use AI-enabled technologies in more detail. Results based on a sample of 223 medical professionals who participated in the online survey revealed that the intention to use AI technologies increases with increasing knowledge about AI and that this effect is moderated by the fear of being replaced by AI.

The perils of hotel technology: The robot usage resistance model

The COVID-19 outbreak has accelerated the development of service robots. However, service robots in some hotels have been put aside despite successful adoption. This study thus focuses on hotel employees' inhibited continuous usage intention by examining the challenges of benefiting from service robots. A robot usage resistance model (RURM) has been proposed based on the results. In this model, lack of authentic anthropomorphous features and low usability as technological characteristics could influence employees' cognitions toward service robots, while robot-related excessive workloads, techno-insecurity, and techno-uncertainty as psychological stimuli could trigger negative emotional arousal, which in turn fosters employee resistance to service robot continuous usage. This study offers a more solid conceptual investigation into employee resistance to service robot continuous usage, thus allowing the development of strategies to better reap the rewards of hotel service robot usage.

Getting Social

Knowledge acquired by end users through their social networks facilitates optimal use of a newly implemented enterprise system. Existing research has conceptualized end users as being the only actors within such networks. Knowledge ties between actors have been treated as unidimensional. The actor-network theory emphasizes the role of all actors in influencing networking outcomes; hence, this study proposes an expanded multimodal social network that includes four institutionally mandated knowledge actors: the technology champions, the help desk, the service desk, and the shared inbox. Knowledge ties are treated as bidimensional through incorporating both technical and business process knowledge. Data collected from an enterprise resource planning system implementation validated this approach; end users sourced knowledge from other end users and the institutionally mandated network actors based on contextual requirements. End user performance outcomes were significantly associated with knowledge source and knowledge dimension.

Factors associated with nurses' user resistance to change of electronic health record systems

Background

Electronic health record (EHR) systems often face user resistance in hospitals, which results in a failure to acquire their full benefits. To implement the EHR successfully, it is crucial to reduce nurses’ resistance to use the system. This study aimed to investigate the factors associated with nurses’ resistance to use the EHR system.

Methods

A descriptive correlational study was conducted with nurses working at four university hospitals in Korea using self-administered questionnaires to measure user resistance behavior, resistance to change, perceived usefulness, perceived ease of use, perceived value, colleagues’ opinions, self-efficacy for change, and organizational support for change. Path analysis was performed to examine direct and indirect association with user resistance behavior.

Results

A total of 223 nurses completed the questionnaires. All seven factors were found to be significantly associated with user resistance, either directly or indirectly. The total effect on user resistance behavior was highest in resistance to change (0.65), followed by perceived usefulness (− 0.33); both had direct but no indirect effects. Conversely, self-efficacy for change (− 0.25), perceived value (− 0.21), colleagues’ opinions (− 0.16), perceived ease of use (− 0.16), and organizational support for change (− 0.05) had indirect but no direct effects.

Conclusions

The study examined the factors associated with nurses’ user resistance behavior after the implementation of a new EHR system. These findings could help hospitals develop better EHR implementation strategies to reduce user resistance behavior among the nursing staff.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12911-021-01581-z.

Discovering the Value Creation System in IoT Ecosystems

Internet of Things (IoT) should not be seen only as a cost reduction mechanism for manufacturing companies; instead, it should be seen as the basis for transition to a new business model that monetizes the data from an intelligent ecosystem. In this regard, deciphering the operation of the value creation system and finding the balance between the digital strategy and the deployment of technological platforms, are the main motivations behind this research. To achieve the proposed objectives, systems theory has been adopted in the conceptualization stage, later, fuzzy logic has been used to structure a subsystem for the evaluation of input parameters. Subsequently, system dynamics have been used to build a computational representation and later, through dynamic simulation, the model has been adjusted according to iterations and the identified limits of the system. Finally, with the obtained set of results, different value creation and capture behaviors have been identified. The simulation model, based on the conceptualization of the system and the mathematical representation of the value function, allows to establish a frame of reference for the evaluation of the behaviour of IoT ecosystems in the context of the connected home.