Improving the Utilization of Digital Services - Evaluating Contest - Driven Open Data Development and the Adoption of Cloud Services

There is a growing interest in utilizing digital services, such as software apps and cloud-based software services. The utilization of digital services is increasing more rapidly than any other segment of world trade. The availability of open data unlocks the possibility of generating market possibilities in the public and private sectors. Digital service utilization can be improved by adopting cloud-based software services and open data innovation for service development. However, open data has no value unless utilized, and little is known about developing digital services using open data. Evaluation of digital service development processes to service deployment is indispensable. Despite this, existing evaluation models are not specifically designed to measure open data innovation contests. Additionally, existing cloud-based digital service implications are not used directly to adopt the technology, and empirical research needs to be included. The research question addressed in this thesis is: "How can contest-driven innovation of open data digital services be evaluated and the adoption of digital services be supported to improve the utilization of digital services?" The research approaches used are design science research, descriptive statistics, and case study. This thesis proposes Digital Innovation Contest Measurement Model (DICM-model) and Designing and Refining DICM (DRD-method) for designing and refining DICM-model to provide more agility. Additionally, a framework of barriers constraining developers of open data services from developing viable services is also presented. This framework enables requirement and cloud engineers to prioritize factors responsible for effective adoption. Future research possibilities are automation of idea generation, ex-post evaluation of the proposed artifacts, and expanding cloud-based digital service adoption from suppliers' perspectives.Comment: The abstract is summarized to fit arxiv's character length requirement; DSV Report Series, Series No. 18-00

DIGITAL PROCTORING IN HIGHER EDUCATION: A SYSTEMATIC LITERATURE REVIEW

To improve the academic integrity of online examination, digital proctoring systems have been implemented in higher education worldwide, particularly during the COVID-19 pandemic. In this paper, we conducted a literature review of the research on digital proctoring in higher education. We found 115 relevant publications in nine databases. We applied topic modeling methods to analyze the corpus which resulted in eight topics. The review shows that the previous studies focus largely on the systems’ development, adoption of the systems, the effects of proctored online exams on students’ performance, and the legal, ethical, security, and privacy issues of digital proctoring. The annual topic trends indicate future research concerns, such as systems’ development, online programs (MOOCs) and proctoring, along with various issues of using digital proctoring. The results of the review provide useful insights as well as implications for future research on digital proctoring, a crucial process for digitalizing higher education

Mapping local patterns of childhood overweight and wasting in low- and middle-income countries between 2000 and 2017

A double burden of malnutrition occurs when individuals, household members or communities experience both undernutrition and overweight. Here, we show geospatial estimates of overweight and wasting prevalence among children under 5 years of age in 105 low- and middle-income countries (LMICs) from 2000 to 2017 and aggregate these to policy-relevant administrative units. Wasting decreased overall across LMICs between 2000 and 2017, from 8.4% (62.3 (55.1–70.8) million) to 6.4% (58.3 (47.6–70.7) million), but is predicted to remain above the World Health Organization’s Global Nutrition Target of <5% in over half of LMICs by 2025. Prevalence of overweight increased from 5.2% (30 (22.8–38.5) million) in 2000 to 6.0% (55.5 (44.8–67.9) million) children aged under 5 years in 2017. Areas most affected by double burden of malnutrition were located in Indonesia, Thailand, southeastern China, Botswana, Cameroon and central Nigeria. Our estimates provide a new perspective to researchers, policy makers and public health agencies in their efforts to address this global childhood syndemic

A toolbox for idea generation and evaluation : Machine learning, data-driven, and contest-driven approaches to support idea generation

Ideas are sources of creativity and innovation, and there is an increasing demand for innovation. For example, the start-up ecosystem has grown in both number and global spread. As a result, established companies need to monitor more start-ups than before and therefore need to find new ways to identify, screen, and collaborate with start-ups. The significance and abundance of data are also increasing due to the growing digital data generated from social media, sensors, scholarly literature, patents, different forms of documents published online, databases, product manuals, etc. Various data sources can be used to generate ideas, yet, in addition to bias, the size of the available digital data is a major challenge when it comes to manual analysis. Hence, human-machine interaction is essential for generating valuable ideas where machine learning and data-driven techniques generate patterns from data and serve human sense-making. However, the use of machine learning and data-driven approaches to generate ideas is a relatively new area. Moreover, it is also possible to stimulate innovation using contest-driven idea generation and evaluation. However, the measurement of contest-driven idea generation processes needs to be supported to manage the process better. In addition, post-contest challenges hinder the development of viable ideas. A mixed-method research methodology is applied to address these challenges. The results and contributions of this thesis can be viewed as a toolbox of idea-generation techniques, including a list of data-driven and machine learning techniques with corresponding data sources and models to support idea generation. In addition, the results include two models, one method and one framework, to better support data-driven and contest-driven idea generation. The beneficiaries of these artefacts are practitioners in data and knowledge engineering, data mining project managers, and innovation agents. Innovation agents include incubators, contest organizers, consultants, innovation accelerators, and industries. Future projects could develop a technical platform to explore and exploit unstructured data using machine learning, visual analytics, network analysis, and bibliometric for supporting idea generation and evaluation activities. It is possible to adapt and integrate methods included in the proposed toolbox in developer platforms to serve as part of an embedded idea management system. Future research could also adapt the framework to barriers that constrain the development required to elicit post-contest digital service. In addition, since the proposed artefacts consist of process models augmented with AI techniques, human-centred AI is a promising area of research that can contribute to the artefacts' further development and promote creativity

Adapting CRISP-DM for Idea Mining : A Data Mining Process for Generating Ideas Using a Textual Dataset

Data mining project managers can benefit from using standard data mining process models. The benefits of using standard process models for data mining, such as the de facto and the most popular, Cross-Industry-Standard-Process model for Data Mining (CRISP-DM) are reduced cost and time. Also, standard models facilitate knowledge transfer, reuse of best practices, and minimize knowledge requirements. On the other hand, to unlock the potential of ever-growing textual data such as publications, patents, social media data, and documents of various forms, digital innovation is increasingly needed. Furthermore, the introduction of cutting-edge machine learning tools and techniques enable the elicitation of ideas. The processing of unstructured textual data to generate new and useful ideas is referred to as idea mining. Existing literature about idea mining merely overlooks the utilization of standard data mining process models. Therefore, the purpose of this paper is to propose a reusable model to generate ideas, CRISP-DM, for Idea Mining (CRISP-IM). The design and development of the CRISP-IM are done following the design science approach. The CRISP-IM facilitates idea generation, through the use of Dynamic Topic Modeling (DTM), unsupervised machine learning, and subsequent statistical analysis on a dataset of scholarly articles. The adapted CRISP-IM can be used to guide the process of identifying trends using scholarly literature datasets or temporally organized patent or any other textual dataset of any domain to elicit ideas. The ex-post evaluation of the CRISP-IM is left for future study

Improving the Utilization of Digital Services - Evaluating Contest-Driven Open Data Development and the Adoption of Cloud Services : Evaluating Contest-Driven Open Data Development and the Adoption of Cloud Services

There is a growing interest in the utilization of digital services, such as software apps and cloud-based software services. The utilization of digital services enabled by ICT is increasing more rapidly than any other segment of the world trade. The availability of open data unlocks the possibility of generating huge market possibilities in the public and private sectors such as manufacturing, transportation, and trade. Digital service utilization can be improved by the adoption of cloud-based software services and through open data innovation for service development. However, open data has no value unless utilized and little is known about the development of digital services using open data. The use of contests to create awareness and call for crowd participation is vital to attract participation for digital service development. Also, digital innovation contests stimulate open data service development and are common means to generate digital services based on open data. Evaluation of digital service development processes stimulated by contests all the way to service deployment is indispensable. In spite of this, existing evaluation models are not specifically designed to measure open data innovation contest. Additionally, existing cloud-based digital service implications, opportunities and challenges, in literature are not prioritized and hence are not usable directly for adoption of cloud-based digital services. Furthermore, empirical research on user implications of cloud-based digital services is missing. Therefore, the purpose of this thesis is to facilitate the utilization of digital services by the adoption of cloud-based digital services and the development of digital services using open data. The main research question addressed in this thesis is: “How can contest-driven innovation of open data digital services be evaluated and the adoption of digital services be supported to improve the utilization of digital services?” The research approaches used are design science research, descriptive statistics, and case study for confirming the validity of the artifacts developed. The design science approach was used to design new artifacts for evaluating open data service development stimulated by contests. The descriptive statistics was applied on two surveys. The first one is for evaluating the implication of cloud-based digital service adoption. While the second one is a longitudinal survey to measure perceived barriers by external open data digital service developers. In this thesis, an evaluation model for digital innovation contest to stimulate service development, (Digital Innovation Contest Measurement Model) DICM-model, and (Designing and Refining DICM) DRD-method for designing and refining DICM-model to provide more agility are proposed. Additionally, the framework of barriers, constraining external developers of open data service, is also presented to better manage service deployment to enable viable service development. Organizers of open data innovation contests and project managers of digital service development are the beneficiaries of these arti-facts. The DICM-model and the DRD-method are used for the evaluation of contest and post contest deployment processes. Finally, the framework of adoption of cloud-based digital services is presented. This framework enables requirement engineers and cloud-based digital service adoption personnel to be able to prioritize factors responsible for an effective adoption. The automation of ideation, which is a key process of digital service development using open data, developer platforms assessment to suggest ways of including evaluation of innovation, ex-post evaluation of the proposed artifacts, and the expansion of cloud-based digital service adoption from the perspectives of sup-pliers are left for further investigations.DSV Report Series Series No. 18-008</p

A toolbox for idea generation and evaluation : Machine learning, data-driven, and contest-driven approaches to support idea generation

Ideas are sources of creativity and innovation, and there is an increasing demand for innovation. For example, the start-up ecosystem has grown in both number and global spread. As a result, established companies need to monitor more start-ups than before and therefore need to find new ways to identify, screen, and collaborate with start-ups. The significance and abundance of data are also increasing due to the growing digital data generated from social media, sensors, scholarly literature, patents, different forms of documents published online, databases, product manuals, etc. Various data sources can be used to generate ideas, yet, in addition to bias, the size of the available digital data is a major challenge when it comes to manual analysis. Hence, human-machine interaction is essential for generating valuable ideas where machine learning and data-driven techniques generate patterns from data and serve human sense-making. However, the use of machine learning and data-driven approaches to generate ideas is a relatively new area. Moreover, it is also possible to stimulate innovation using contest-driven idea generation and evaluation. However, the measurement of contest-driven idea generation processes needs to be supported to manage the process better. In addition, post-contest challenges hinder the development of viable ideas. A mixed-method research methodology is applied to address these challenges. The results and contributions of this thesis can be viewed as a toolbox of idea-generation techniques, including a list of data-driven and machine learning techniques with corresponding data sources and models to support idea generation. In addition, the results include two models, one method and one framework, to better support data-driven and contest-driven idea generation. The beneficiaries of these artefacts are practitioners in data and knowledge engineering, data mining project managers, and innovation agents. Innovation agents include incubators, contest organizers, consultants, innovation accelerators, and industries. Future projects could develop a technical platform to explore and exploit unstructured data using machine learning, visual analytics, network analysis, and bibliometric for supporting idea generation and evaluation activities. It is possible to adapt and integrate methods included in the proposed toolbox in developer platforms to serve as part of an embedded idea management system. Future research could also adapt the framework to barriers that constrain the development required to elicit post-contest digital service. In addition, since the proposed artefacts consist of process models augmented with AI techniques, human-centred AI is a promising area of research that can contribute to the artefacts' further development and promote creativity

A Systematic Literature Review about Idea Mining : The Use of Machine-Driven Analytics to Generate Ideas

Idea generation is the core activity of innovation. Digital data sources, which are sources of innovation, such as patents, publications, social media, websites, etc., are increasingly growing at unprecedented volume. Manual idea generation is time-consuming and is affected by the subjectivity of the individuals involved. Therefore, the use machine-driven data analytics techniques to analyze data to generate ideas and support idea generation by serving users is useful. The objective of this study is to study state-of the-art machine-driven analytics for idea generation and data sources, hence the result of this study will generally serve as a guideline for choosing techniques and data sources. A systematic literature review is conducted to identify relevant scholarly literature from IEEE, Scopus, Web of Science and Google Scholar. We selected a total of 71 articles and analyzed them thematically. The results of this study indicate that idea generation through machine-driven analytics applies text mining, information retrieval (IR), artificial intelligence (AI), deep learning, machine learning, statistical techniques, natural language processing (NLP), NLP-based morphological analysis, network analysis, and bibliometric to support idea generation. The results include a list of techniques and procedures in idea generation through machine-driven idea analytics. Additionally, characterization and heuristics used in idea generation are summarized. For the future, tools designed to generate ideas could be explored.

Debriefing for Knowledge Management

Since the term was coined in the 1970s, debriefing has been associated with military campaigns, critical incidents and accidents. Debriefing has also been used in the health sector and educational settings particularly experience-based learning. However, the application of debriefing for knowledge management is a recent phenomenon which did not attract the attention of many researchers. As knowledge management is considered to be one of the important issues for today’s firms, our understanding of the available tools that could be used to improve the identification, creation and sharing of knowledge in an organization is necessary and timely. The use of debriefing as a simple, straightforward tool which requires the deployment of resources that are available within the boundaries of organization—knowledge, skill and expertise of employees—is acknowledged. However, there is still a lack of knowledge on how organizations can successfully design, plan and execute debriefing to manage knowledge. This paper is poised to provide an overview of studies on debriefing through the lens of knowledge management. The study contributes to the information systems discipline by revealing the significance of debriefing for effective knowledge management practice based on literature review of previous studies. The study also provides potential future research directions.

Contextualizing the rural in digital studies : A computational literature review of rural-digital relations

Digital technologies are changing how and where we live, work and socialize. Rural areas are distinctive spaces and places but in the current debates of new digital phenomena, digital spaces and practices risk not being contextualized with sensitivities to rural geographies. This study aims to map how digital has been examined to date in rural-focused studies, and accordingly present propositions for how rural-digital studies can be sensitive to the distinctive and diverse character of rural spaces and places. We conduct a two-stage/scale literature review, combining 1) computational topic modelling from a Global Dataset (459 article abstracts) with 2) qualitative content analysis from a sub-dataset focusing on the Nordic region (Nordic Sub-Dataset, 17 full articles). We begin with a topic modelling analysis generating ten major themes (topics) leading to an overview of how research areas are connected to the meaning of rural context. Turning to the Nordic region, as an in-depth example, we illustrate the complexity of rural digital geographies, through a qualitative content analysis. This demonstrates that digital in rural contexts are primarily positioned outwardly as social/regional development and business/economy, and less situated inwardly through individual experience and community building. Combined we show a wide spectrum of rural-digital relations but demonstrate that rural contexts in rural-digital relations need more attention. We propose three propositions to invite deeper rural contextualizations in future digital studies to uphold the importance of rural spaces and places through, by and with digital geography