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Organizational use of information and communications technologies (ICT) is increasingly resulting in negative cognitions in individuals, such as information overload and interruptions. Recent literature has encapsulated these cognitions in the concept of technostress, which is stress caused by an inability to cope with the demands of organizational computer usage. Given the critical role of the user in organizational information processing and accomplishing application-enabled workflows, understanding how these cognitions affect users' satisfaction with ICT and their performance in ICT-mediated tasks is an important step in appropriating benefits from current computing environments. The objective of this paper is to (1) understand the negative effects of technostress on the extent to which end users perceive the applications they use to be satisfactory and can utilize them to improve their performance at work and (2) identify mechanisms that can mitigate these effects. Specifically, we draw from the end-user computing and technostress literature to develop and validate a model that analyzes the effects of factors that create technostress on the individual's satisfaction with, and task performance using, ICT. The model also examines how user involvement in ICT development and support mechanisms for innovation can be used to weaken technostress-creating factors and their outcomes. The results, based on survey data analysis from 233 ICT users from two organizations, show that factors that create technostress reduce the satisfaction of individuals with the ICT they use and the extent to which they can utilize ICT for productivity and innovation in their tasks. Mechanisms that facilitate involvement of users, and encourage them to take risks, learn, explore new ideas, and experiment in the context of ICT use, diminish the factors that create technostress and increase satisfaction with the ICT they use. These mechanisms also have a positive effect on users' appropriation of ICT for productivity and innovation in their tasks. The paper contributes to emerging literature on negative outcomes of ICT use by (1) highlighting the influence of technostress on users' satisfaction and performance (i.e., productivity and innovation in ICT-mediated tasks) with ICT, (2) extending the literature on technostress, which has so far looked largely at the general behavioral and psychological domains, to include the domain of end-user computing, and (3) demonstrating the importance of user involvement and innovation support mechanisms in reducing technostress-creating conditions and their ICT use-related outcomes.

Machine learning is disruptive. At the same time, machine learning can only succeed by collaboration among many parties in multiple steps naturally as pipelines in an eco-system, such as collecting data for possible machine learning applications, collaboratively training models by multiple parties and delivering machine learning services to end users. Data are critical and penetrating in the whole machine learning pipelines. As machine learning pipelines involve many parties and, in order to be successful, have to form a constructive and dynamic eco-system, marketplaces and data pricing are fundamental in connecting and facilitating those many parties. In this article, we survey the principles and the latest research development of data pricing in machine learning pipelines. We start with a brief review of data marketplaces and pricing desiderata. Then, we focus on pricing in three important steps in machine learning pipelines. To understand pricing in the step of training data collection, we review pricing raw data sets and data labels. We also investigate pricing in the step of collaborative training of machine learning models and overview pricing machine learning models for end users in the step of machine learning deployment. We also discuss a series of possible future directions.

Objective The aim of this study was to describe the process used to develop a theory‐based, online fall prevention self‐management programme for ambulatory and non‐ambulatory people with multiple sclerosis (pwMS). Methods The development process was guided by the Medical Research Council framework of complex interventions and began with a scoping review of the literature on self‐management of falls in pwMS. Subsequent phases of development were performed through iterative and concurrent processes and were informed by the perspectives of pwMS and healthcare professionals with MS expertise. Results Through a systematic and iterative process in close collaboration with pwMS and healthcare professionals, a theory‐based online fall prevention self‐management programme, Fewer Falls in MS, for ambulatory and non‐ambulatory pwMS was developed. The programme is grounded in theory and pedagogical models and features utilization of action plans to address diverse influences on fall risks. Conclusions A carefully operationalized definition of self‐management and an iterative co‐development process were essential to the creation of the Fewer falls in MS programme. Continuation of the co‐development process and collaboration with end users was needed to refine the programme. Patient or Public Contribution PwMS and healthcare professionals were involved throughout the development process of the programme. The patient organization Neuro Sweden was contacted in the initial phase to discuss the relevance of a self‐management programme to prevent falls in MS. They supported the research group (all authors) in identification of and contact with pwMS with interest to participate. Three members of the research group (S.T.J., M.F. and C.Y.), that is, the operative group, met neuro Sweden and one pwMS to further discuss the relevance of a self‐management programme to prevent falls. To develop the process and content of the fall prevention programme, a co‐design process was performed together with pwMS and healthcare professionals. The results of the co‐design process are presented in this manuscript. In addition to participating in the co‐design process, pwMS and healthcare professionals provided feedback to the research group on programme process and content on several occasions during the subsequent programme development process. In a pretest (Beta version) of the programme, four pwMS acted as test subjects and provided additional feedback on the programme to the research group. Trial Registration NCT04317716.

Building predictive models for human-interactive systems is a challenging task. Every individual has unique characteristics and behaviors. A generic human–machine system will not perform equally well for each user given the between-user differences. Alternatively, a system built specifically for each particular user will perform closer to the optimum. However, such a system would require more training data for every specific user, thus hindering its applicability for real-world scenarios. Collecting training data can be time consuming and expensive. For example, in clinical applications it can take weeks or months until enough data is collected to start training machine learning models. End users expect to start receiving quality feedback from a given system as soon as possible without having to rely on time consuming calibration and training procedures. In this work, we build and test user-adaptive models (UAM) which are predictive models that adapt to each users’ characteristics and behaviors with reduced training data. Our UAM are trained using deep transfer learning and data augmentation and were tested on two public datasets. The first one is an activity recognition dataset from accelerometer data. The second one is an emotion recognition dataset from speech recordings. Our results show that the UAM have a significant increase in recognition performance with reduced training data with respect to a general model. Furthermore, we show that individual characteristics such as gender can influence the models’ performance.

Background Exergames are playful technology-based exercise programs. They train physical and cognitive functions to preserve independence in older adults (OAs) with disabilities in daily activities and may reduce their risk of falling. This study gathered in-depth knowledge and understanding of three different user groups’ experiences in and relevant needs, worries, preferences, and expectations of technology-based training, to develop an exergame training device for OAs. Methods We conducted a qualitative study using semi-structured focus group interviews of primary (OAs in geriatric or neurological rehabilitation) and secondary (health professionals) end users, as well as expert interviews of tertiary end users (health insurance experts or similar), exploring user perspectives on adjusting an existing exergame to OAs’ needs. Voice-recorded interviews were transcribed by researchers and analyzed using thematic analysis (TA) following an inductive, data-driven, iterative approach. Results We interviewed 24 primary, 18 secondary, and 9 tertiary end users at two rehabilitation centers in Austria and Switzerland. Our TA approach identified five to six themes per user group. Themes in the primary end user group reflected aspects of safety, training goals, individuality, game environment, social interactions, and physical and technical overload. Themes in the secondary end user group comprised facets of meaningfulness, distraction through the game environment, safety, gamification elements, the availability and accessibility of the exergame. Tertiary end users’ themes addressed aspects of financial reimbursement, suitable target populations, professional training for the handling of exergame devices, training goals, and concerns about the use of exergames in geriatric rehabilitation. Conclusions In conclusion, an exergame for OAs must be safe, motivating and fully adaptable to the target group while promoting the return to or preservation of autonomy and independence in daily life. Our findings contribute to developing hard- and software extensions for the ExerG training device. Further research is needed to expand the validity of our findings to larger populations.

The prevalence of disability is on the rise globally and in South Africa, with a high number of unmet needs and poor actualisation of the health rights of persons with disabilities. A tool to realise these rights is health policy, such as the framework and strategy for disability and rehabilitation (2015-2022)(FSDR). There are limited data on the implementation outcomes of the FSDR. To review the implementation of the FSDR according to the paper-based provincial reports. The study conducted a document review and utilised a concurrent mixed-method design, combining qualitative and quantitative data extracted from paper-based evaluation templates developed by the South African National Department of Health (NDoH). The qualitative analysis involved thematic coding using the RE-AIM framework to examine the FSDR's implementation across eight provinces, while quantitative data, such as frequencies and percentages, provided supplementary insights. The quantitative results revealed that 87% of the reports from provinces reported physical accessibility to the FSDR, and 62% of provinces received training on the implementation of the FSDR. Only two out of eight provinces have conducted monitoring and evaluation since implementing the FSDR in 2015. Qualitative findings revealed poor reach and adoption of the FSDR owing to a lack of implementation training for end users. The lack of indicators resulted in poor maintenance of the FSDR, as well as the lack of human resources and equipment which resulted in the reduced efficacy of the FSDR. The FSDR has not achieved its full level of implementation due to numerous barriers, such as lack of resources, human capacity, and training on implementation.

The end-user paradox and the illusion of digital prosperity reveal the contradictory situation in which both non-professional and professional computer scientists and engineers seem satisfied with digital development but unaware of the magnitude of waste generated by end-users and their digital artifacts. To measure this waste and to reveal end-users’ problem-solving strategies, our research group set up an objective measuring system that can calculate the entropy of digital texts (EDT). To calculate EDT, a testing process of 53 participants was launched where erroneous and correct natural language digital texts were modified according to the requirements of the tasks. It was found that erroneous documents require more time and information to be modified, which implies that waste is generated by handling these documents. It was also found that when the problem-solving processes are broken down into atomic steps, EDT can reveal uncertainty and idleness, which further increases waste. The goals of the present paper are to call attention to (1) the hidden waste generated by billions of end-users and its consequences, (2) educational approaches and general ignorance which have led to these low-level results, and (3) the need to set up a standard evaluation system for further analyses.

The fairness of vocational contest scoring is key to generating reliable competency assessments. This study examined the performance impact of the motivation of English-as-a-foreign-language learners in contests with vocabulary knowledge antecedents in the contexts of artificial intelligence (AI) and blockchain (BC). The sample comprised 185 participants of an oral English contest at higher vocational institution in China. AI-powered scoring of learners' contest performance and a survey were used to collect data. The findings revealed that learners' intrinsic drive was the main positive factor, outweighing their extrinsic motivation, and that AI and BC increased the trustworthiness and integrity of contest records, thus providing new opportunities to build learner trust and form psychological incentives. This study enriches foreign language motivation theory in the context of contest research and highlights the importance of using AI and BC to enhance the scoring accuracy and credibility of contests as authoritative evaluation instruments in vocational education.

The deregulation of the electricity markets in the European Union and the transformation caused by digital technologies and customer-centric strategies have altered the industry ecosystem, forcing companies to adapt to the new scenario. This research study aims to give a global overview of the digital transformation and channel integration of free-market electricity retailers in Spain from a consumer’s perspective. The analysis includes all free-market electricity retailers that operate at the national scale, explores the level of digital transformation and channel integration of these companies based on a structured set of indicators, and measures them using the mystery shopper technique. The results show important differences between leading retailers and the rest of companies, evidence an important lag of the sector when compared to other retail markets and an overall lack of multichannel and omnichannel strategies, show a strong effort of retailers in online billing and self-service customer data management, and reveal shortcomings in the availability of communication channels with customers.

Most organizations assessing Generative AI (Gen AI) rely on assessment materials produced by AI providers. These tend to be technical. To create a recommended sociotechnical assessment, the technical needs to be coupled with the social. This policy editorial suggests taking advantage of the knowledge of end users who are likely to use Gen AI in their work and provides an illustrative framework for doing so.