Compatible quality of social media content: Conceptualization,measurement, and affordances

Building and maintaining favorable social media relationships with customers require that organizations produce quality content that fits customers’ needs. So far, little if any research has conceptualized and measured this kind of content quality in the context of social media. Therefore, this study proposes and empirically examines a new construct, compatible quality of social media content, which adds to knowledge by expanding prior online content quality research beyond the traditional focus on non-fit quality beliefs. The results of structural equation modeling of responses collected from a sample of active social media users reveal at least three worth noting findings. First, the study confirmed the multidimensionality of compatible quality of social media content, which encompasses reflective, stimulated, practiced, and advocated components. Second, there was equivalence in the measurements of the studied constructs and the structural weights of the proposed network of relationships across the different gender and experience subgroups. Third, compatible quality of social media content was found to influence continued interest, active confidence, and feedback openness. The practical and research implications of these results are discussed.     

Changes in roles,responsibilities and ownership in organizing master data management

Master data management (MDM) is a data management practice, which attempts to increase data quality and data use across business processes throughout the organization. This paper observes how data ownership, responsibilities, and roles change during MDM development. The metaphor of imbrication was used as a theoretical lens to identify the factors that influenced the change, and to analyze the change as a result of the intertwined social and material factors. We derive ethnographical data from two MDM projects in a municipality over a time period of 32 months, and describe how data ownership and data governance roles and responsibilities were perceived, and how they evolved during the development. As a result, MDM data ownership is emphasized, and has distinct features in relation to roles and responsibilities. Ownership had on impact on how the development proceeded, and how the roles and responsibilities evolved.     

Virtual internships in blended environments to prepare preservice teachers for the professional teaching context

This study investigated to what extent virtual internships in teacher education were able to reduce Preservice Teachers’ (PSTs) professional anxiety. Simultaneously, this study investigated how virtual internships in blended learning environments were evaluated by PSTs in terms of technological, social and educational affordances. PSTs followed virtual internships during two different Educational Pedagogy Master’s courses (27 and 16 participants) in a teacher education programme. A mixed methods design was employed, consisting of pre- and post-test questionnaires, a focus group interview and individual interviews. A significant decrease was found in PSTs’ professional anxiety after having followed Virtual Internship 2. PSTs reported they obtained a more realistic image of teaching and felt better prepared for teaching in practice. Furthermore, regarding technological affordances, system usability was considered between acceptable and good. Concerning social affordances, PSTs appreciated collaboration within the virtual internships. As an educational affordance, it appeared that learning from videos with authentic classroom events is a good preparation for the professional teaching context. According to the PSTs, the scenarios within virtual internships could be improved in terms of authenticity and personalisation by offering more details and background information. The results of this study imply that virtual internships can be useful assets for teacher education.     

Exploring the factors,affordances and constraints outlining the implementation of Artificial Intelligence in public sector organizations

Artificial Intelligence (AI) is viewed as having great potential for the public sector to improve the management of internal activities and the delivery of public services. However, realizing its potential depends on the proper implementation of the technology, which is characterized by unique factors, that afford or constrain its use. What these factors are and how they affect AI implementation is still poorly understood, and scholars call for studies to add empirical evidence to the existing knowledge. This study relies on a case study methodology and, by adopting an abductive approach, applies a double theoretical perspective: the Technology-Organization-Environment (TOE) framework and the Technology Affordances and Constraints Theory (TACT). Drawing on these combined lenses, we develop a conceptual framework that extends previous studies by showing how AI implementation is the result of a combination of contextual factors that are deeply interrelated and, specifically, how AI-related factors bring new affordances and constraints to the application domain.