UNDERSTANDING THE VALUE OF SOCIAL MEDIA IN ORGANISATIONS: A TAXONOMIC APPROACH

While organizations strive to leverage the vast information generated daily from social media platforms and both decision makers and consultants are keen to identify and exploit this information’s value, there has been little research into social media in the business context. Social media are diverse, varying in scope and functionality, this diversity entailing a complex of attributes and characteristics, resulting in confusion for both researchers and organizations. Taxonomies are important precursors in emerging fields and are foundational for rigorous theory building. Though aspects of social media have been studied from various discipline perspectives, this work has been largely descriptive. Thus, while the need for a rigorous taxonomy of social media is strong, previous efforts to classify social media suffer limitations – e.g. lack of a systematic taxonomic method, overreliance on intuition, disregard for the users’ perspective, and inadequate consideration of purpose. Thus, this study was mainly initiated by the overarching question “How can social media in the business context be usefully classified?” In order to address this gap, the current paper proposes a systematic method for developing a taxonomy appropriate to study social media in organizations context, combining Nickerson et al,’s (2012) IS taxonomy building guidelines and a Repertory grid (RepGrid) approach

A theoretical approach to conceptualize information quality in social media

While organizations strive to leverage the vast information generated daily from social media platforms, and decision makers are keen to identify and exploit its value, the quality of this information remains uncertain. Past research on information quality criteria and evaluation issues in social media is largely disparate, incomparable and lacking any common theoretical basis. In attention to this gap, this study adapts existing guidelines and exemplars of construct conceptualization in information systems research, to deductively define information quality and related criteria in the social media context. Building on a notion of information derived from semiotic theory, this paper suggests a general conceptualization of information quality in the social media context that can be used in future research to develop more context specific conceptual models

Re-conceptualisation of information quality : a critical realist perspective

This thesis presents detailed methodological guidelines and a comprehensive conceptual framework to guide and to harmonize studies on information quality as a multi-disciplinary object of study. Despite the abundance of literature on information quality, this phenomenon has not been defined clearly and research efforts have failed to address practical challenges resulting from complex contextual situations and technological advancements. This thesis addresses this issue by building a meta-framework for conceptualizing information quality to align research efforts with real world problems. This framework is built on the tenets of applied Critical Realism, Peircian Semiotics and social science methodological guidelines

Managing trust - A design theory and design principles

Technological developments along with socio-economical changes (e.g., COVID-19) have increased the trust intensity of social and business interactions and have created new trust concerns. On the other hand, the affordances of new technologies facilitate new trust opportunities. Trust is one of the well-cited and popular research areas in Information Systems research. However, despite the depth and breadth of knowledge on trust and related concepts, it remains an elusive concept in practice. We know more about what trust is than how to deal with it. Accordingly, in the absence of a prescriptive trust theory, drawing on existing trust literature and the expertise of senior stakeholders from different companies, we develop a design theory for trust, proposing complementary design principles to build trust into the processes of customers’ interaction with a company, considering contemporary trust concerns and leveraging the opportunities provided by new technologies

Managing trust- A design theory and design principles

Technological developments along with socio-economical changes (e.g., COVID-19) have increased the trust intensity of social and business interactions and have created new trust concerns. On the other hand, the affordances of new technologies facilitate new trust opportunities. Trust is one of the well-cited and popular research areas in Information Systems research. However, despite the depth and breadth of knowledge on trust and related concepts, it remains an elusive concept in practice. We know more about what trust is than how to deal with it. Accordingly, in the absence of a prescriptive trust theory, drawing on existing trust literature and the expertise of senior stakeholders from different companies, we develop a design theory for trust, proposing complementary design principles to build trust into the processes of customers’ interaction with a company, considering contemporary trust concerns and leveraging the opportunities provided by new technologies

Information quality in social media: A conceptual model

Social Media (SM) is increasingly being integrated with business information in decision making. Unique characteristics of social media (e.g. wide accessibility, permanence, global audience, recentness, and ease of use) raise new issues with information quality (IQ); quite different from traditional considerations of IQ in information systems (IS) evaluation. This paper presents a preliminary conceptual model of information quality in social media (IQnSM) derived through directed content analysis and employing characteristics of analytic theory in the study protocol. Based in the notion of ‘fitness for use’, IQnSM is highly use and user centric and is defined as “the degree to which information is suitable for doing a specified task by a specific user, in a certain context”. IQnSM is operationalised as hierarchical, formed by the three dimensions (18 measures): intrinsic quality, contextual quality and representational quality. A research plan for empirically validating the model is proposed

Data Governance for Managing Data Quality in Process Mining

Process mining, a specialised form of data-driven process analytics, is concerned with evidence-based process improvement. Process mining relies on process data, which often suffers from data quality issues that may be hard to detect and rectify. Data governance, recognised as a business capability, was recently introduced to manage data, including its quality, to maximise data's tactical value. Interestingly, no tailored data governance approach for managing process-data quality exists. The paper bridges this gap by introducing a data governance framework, the ImperoPD framework, for process mining with a focus on data quality. We use a capability-based approach and conduct a theoretical review of 75 papers to identify 20 capabilities an organisation should possess to implement process-data governance successfully. The framework is validated for its utility and comprehensiveness by 11 data governance experts. It contributes to an understanding of what is required to implement a data governance program for process mining

Re-Thinking the Ontology of Information

Information, though a core concept in Information System research, has been taken for granted by scholars for many years. Recent studies have attempted to shed light on this concept by classifying information theories and proposing new conceptualizations

An expert lens on data quality in process mining

The success of a process mining project is highly dependent on the quality of the event log data, the degree to which quality issues are detected, and the way they are resolved. The detection and resolution of data quality issues requires a systematic approach that is aware of the organisational context in which event log data is created. To this end, the Odigos framework has been developed in prior work. The focus of this paper is the validation of this framework through semistructured interviews with a range of experts in process mining. The experts confirmed the utility of the framework, provided valuable insights into data quality in practical settings, and suggested enhancements to the Odigos framework. </p

Spatial Data in Urban Informatics: Contentions of the Software-Sorted City

Cities worldwide are at a critical juncture as digital technology, ubiquitous computing and IoT devices have been entering all aspects of urban life resulting in so-called smart cities, which use urban informatics and urban science to collect, process and analyse spatial big data using machine learning and artificial intelligence (AI). The aim of this chapter is to offer a critical look at spatial data applications in urban informatics and urban science. We draw on an empirical investigation into location awareness and the politics of spatial data, which explored both end-user and industry/government perceptions of location-based services and the digital platforms providing them. After introducing relevant literature, we briefly outline the methodology that produced the empirical data we rely on here. In keeping with our focus on contentions of the software-sorted city, we discuss three broad themes: (1) big data versus thick data; (2) data privacy, and; (3) data sovereignty. We conclude with an outlook on future work