| 1. |
- Ayele, Workneh Y., et al.
(författare)
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A Method for Designing Digital Innovation Contest Measurement Models
- 2016
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Konferensbidrag (refereegranskat)abstract
- As contests become more popular means for organizing digital innovation, the need for measuring contest performance increases. The Digital Innovation Contest Measurement-model (DICM-model), which is the basis for this study was designed based on a single case study, and its evaluation indicated that there is a need for a customizable methodological approach that can accommodate differences in organizational requirements for designing and refining DICM-models. Therefore, in this paper, we present a summary of the evaluation of the DICM-model and propose a nine-step method to design and refine DICM-models using a quality oriented approach. The proposed method is based on the Goal-Question-Metric and the Balanced Scorecard to elicit measures and to enable agility in measuring the fulfilment of measurement goals of innovation contests. Also, the method facilitates knowledge management to refine, record and communicate best practices. An exante evaluation of the method indicates that the method provides practical support in designing and improving a DICM model. For future study, it is suggested to widen the scope of the method to aid in the design of measurement models for digital innovations using open data, in general.
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| 2. |
- Ayele, Workneh Y., et al.
(författare)
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A Process Model for Generating and Evaluating Ideas : The Use of Machine Learning and Visual Analytics to Support Idea Mining
- 2020
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Ingår i: Electronic Government and the Information Systems Perspective. - Cham : Springer. - 9783030589561 - 9783030589578 ; , s. 189-203
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Konferensbidrag (refereegranskat)abstract
- The significance and possibilities of idea generation and evaluation are increasing due to the increasing demands for digital innovation and the abundance of textual data. Textual data such as social media, publications, patents, documents, etc. are used to generate ideas, yet manual analysis is affected by bias and subjectivity. Machine learning and visual analytics tools could be used to support idea generation and evaluation, referred to as idea mining, to unlock the potential of voluminous textual data. Idea mining is applied to support the extraction of useful information from textual data. However, existing literature merely focuses on the outcome and overlooks structuring and standardizing the process itself. In this paper, to support idea mining, we designed a model following design science research, which overlaps with the Cross-Industry-Standard-Process for Data Mining (CRISP-DM) process and adapts well-established models for technology scouting. The first layer presents and business layer, where tasks performed by technology scouts, incubators, accelerators, consultants, and contest managers are detailed. The second layer presents the technical layer where tasks performed by data scientists, data engineers, and similar experts are detailed overlapping with CRISP-DM. For future research, we suggest an ex-post evaluation and customization of the model to other techniques of idea mining.
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| 3. |
- Ayele, Workneh Y., et al.
(författare)
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Eliciting Evolving Topics, Trends and Foresight about Self-driving Cars Using Dynamic Topic Modeling
- 2020
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Ingår i: Advances in Information and Communication. - Cham : Springer. - 9783030394448 - 9783030394455 ; , s. 488-509
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Konferensbidrag (refereegranskat)abstract
- Self-driving technology is part of smart city ecosystems, and it touches a broader research domain. There are advantages associated with using this technology, such as improved quality of life, reduced pollution, and reduced fuel cost to name a few. However, there are emerging concerns, such as the impact of this technology on transportation systems, safety, trust, affordability, control, etc. Furthermore, self-driving cars depend on highly complex algorithms. The purpose of this research is to identify research agendas and innovative ideas using unsupervised machine learning, dynamic topic modeling, and to identify the evolution of topics and emerging trends. The identified trends can be used to guide academia, innovation intermediaries, R&D centers, and the auto industry in eliciting and evaluating ideas. The research agendas and innovative ideas identified are related to intelligent transportation, computer vision, control and safety, sensor design and use, machine learning and algorithms, navigation, and human-driver interaction. The result of this study shows that trending terms are safety, trust, transportation system (traffic, modeling traffic, parking, roads, power utilization, the buzzword smart, shared resources), design for the disabled, steering and control, requirement handling, machine learning, LIDAR (Light Detection And Ranging) sensor, real-time 3D image processing, navigation, and others.
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| 4. |
- Ayele, Workneh Y., et al.
(författare)
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Identifying Emerging Trends and Temporal Patterns About Self-driving Cars in Scientific Literature
- 2020
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Ingår i: Advances in Computer Vision. - Cham : Springer. - 9783030177973 - 9783030177980 ; , s. 355-372
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Konferensbidrag (refereegranskat)abstract
- Self-driving is an emerging technology which has several benefits such as improved quality of life, crash reductions, and fuel efficiency. There are however concerns regarding the utilization of self-driving technology such as affordability, safety, control, and liabilities. There is an increased effort in research centers, academia, and the industry to advance every sphere of science and technology yet it is getting harder to find innovative ideas. However, there is untapped potential to analyze the increasing research results using visual analytics, scientometrics, and machine learning. In this paper, we used scientific literature database, Scopus to collect relevant dataset and applied a visual analytics tool, CiteSpace, to conduct co-citation clustering, term burst detection, time series analysis to identify emerging trends, and analysis of global impacts and collaboration. Also, we applied unsupervised topic modeling, Latent Dirichlet Allocation (LDA) to identify hidden topics for gaining more insight about topics regarding self-driving technology. The results show emerging trends relevant to self-driving technology and global and regional collaboration between countries. Moreover, the result form the LDA shows that standard topic modeling reveals hidden topics without trend information. We believe that the result of this study indicates key technological areas and research domains which are the hot spots of the technology. For the future, we plan to include dynamic topic modeling to identify trends.
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| 5. |
- Ayele, Workneh Y., et al.
(författare)
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Unveiling DRD : A Method for Designing Digital Innovation Contest Measurement Models
- 2018
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Ingår i: Systems, Signs & Actions. - 1652-8719. ; 11:1, s. 25-53
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Tidskriftsartikel (refereegranskat)abstract
- The growing open data market opens possibilities for the development of viable digital artifacts that facilitate the creation of social and business values. Contests are becoming popular means to facilitate the development of digital artifacts utilizing open data. The increasing popularity of contests gives rise to a need for measuring contest performance. However, the available measurement model for digital innovation contests, the DICM-model, was designed based on a single case study and there is a need for a methodological approach that can accommodate for contests’ variations in scope. Therefore, we use design science to construct a nine-step method, the DRD method, to design and refine DICM-models. The DRD-method is designed using goal- and quality oriented approaches. It extends innovation measurement to the application domain of digital innovation contests and provides an improvement of innovation measurement as it offers a new solution for a known problem. The DRD-method provides comprehensive support to practice for designing and refining DICM-models and supports reflection and organizational learning across several contests. For future study, we suggest an ex-post evaluation of the method inconjunction with real contests and systematic efforts to generalize the method within as well as beyond the context of the contest. Finally, we propose to further investigate the potential of topdown and goal oriented approaches to measure open and iterative forms of innovation.
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| 6. |
- Ayele, Workneh Y., et al.
(författare)
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Unveiling Topics from Scientific Literature on the Subject of Self-driving Cars using Latent Dirichlet Allocation
- 2018
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Ingår i: 2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON). - : IEEE. - 9781538672679 - 9781538672662 ; , s. 1113-1119
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Konferensbidrag (refereegranskat)abstract
- Self-driving cars are becoming popular topics in academia. Consumers of self-driving cars and vehicles have different concerns, for example, safety and security, to name a few. Also, the public sector has interests in self-driving cars such as amending policies to enable the management of self-driving vehicles in cities, urban planning, traffic management and, etc. In this paper, more than 2700 corpus are extracted from literature from several subject areas to identify latent (hidden) topics of self-driving cars. Latent Dirichlet Allocation (LDA) is used for topic identification. The result of this study shows that topics identified are valid research areas such as urban planning, driver car (computer) interaction, self-driving control and system design, ethics in self-driving cars, safety and risk assessment, training dataset quality and machine learning in self-driving cars are among the topics identified. Furthermore, the network visualization of association graph of terms shows that the most frequently discussed concepts reveal that control of self-driving cars is based on algorithms, data, design, method, and model. The methods used in this study and the results can be used as decision tools, if carefully applied, in diverse disciplines that are disrupted by the introduction of self-driving cars. For future study, we plan to extend this study with a larger dataset and other data mining techniques.
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| 7. |
- Ayele, Workneh Y., et al.
(författare)
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User Implications for Cloud Based Public Information Systems : A Survey of Swedish Municipalities
- 2015
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Ingår i: Proceedings of the 2015 2nd International Conference on Electronic Governance and Open Society. - New York, NY, USA : Association for Computing Machinery (ACM). - 9781450340700 ; , s. 217-227
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Konferensbidrag (refereegranskat)abstract
- The emergence of cloud computing has implications for digital service provision in the private and public sector. These implications can introduce opportunities and challenges for user organizations. Evaluation of implications by prioritization and reduction of variables aids in procurement and adoption of cloud based public information systems. However, so far little research is available to evaluate implications of cloud computing in the public sector.The evaluation of implications is carried out through a survey of Swedish municipalities. Quantitatively summarizing the collected data a list of prioritized implications were obtained. In addition to this through a statistical analysis technique called exploratory factor analysis the number of implications are reduced by grouping them into factors.The result shows that the most significant implications for cloud based public information systems are remote access from anywhere at any time, access to and flexibility to choose between state of the art technologies as well as large dependency on vendor and less customization possibilities. A prioritized list of implications is presented from the perspective of users of cloud based public information systems. Through factor analysis we are able to reduce the number of opportunities to six and challenges to four. For future research we suggest to evaluate implications of cloud based public information systems from suppliers' perspective.
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| 8. |
- Han, Shengnan, et al.
(författare)
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Digital proctoring in higher education : a systematic literature review
- 2022
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Ingår i: ECIS 2022 Proceedings. - : Association for Information Systems (AIS).
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Konferensbidrag (refereegranskat)abstract
- 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 asimplications for future research on digital proctoring, a crucial process for digitalizing higher education.
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| 9. |
- Hjalmarsson, Anders, et al.
(författare)
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From contest to market entry : A longitudinal survey of innovation barriers constraining open data service development
- 2015
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Ingår i: ECIS 2015 Completed Research Papers. - : Association for Information Systems. - 9783000502842
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Konferensbidrag (refereegranskat)abstract
- Open data services have emerged as a research field. One important area of investigation within this field is exploration into how sustainable open data markets are created. Contests have become a popular method to propel and catalyse open data service development providing services to such markets. Recent research has identified numerous innovation barriers hampering development adjacent to the contest in developers' effort to transform contest contributions to viable digital services based on open data. Little is however known about what innovation barriers over time constrain the post-contest process to transform initial innovations to finalized open data services ready for market entry. This paper presents a longitudinal survey of innovation barriers constraining teams performing open data service development after an innovation contest. The survey provides insights into 1) 24 innovation barriers constraining development, 2) a comparison of barrier importance based on team progress, and 3) a conceptualisation of phases structuring the process from contests to market entry, stipulating different innovation barriers impact open data service development. The results contribute to the understanding of how sustainable open data markets emerge and serve as a starting point for investigating how different stakeholders can manage innovation barriers constraining open data service development.
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| 10. |
- Leffler, Linus, et al.
(författare)
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Robotic Process Automation for Reducing Food Wastage in Swedish Grocery Stores
- 2023
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Ingår i: Advances in Information and Communication. - Cham : Springer. - 9783031280726 - 9783031280733 ; , s. 875-892
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Konferensbidrag (refereegranskat)abstract
- In the wake of global warming and ever-increasing conflicts and instabilities for controlling resources and interests hampering agricultural development, food security has been the main priority of nations. Besides, food wastage is a global issue that has become more serious. For example, the Food and Agriculture Organization (FAO) of the United Nations reports that it aims to reduce global food wastage in production, retail, and supply chain. In Sweden, roughly 100,000 tons of food is wasted every year due to the date of expiry in grocery stores, according to a government report published in 2020. On the other hand, Robotic Process Automation (RPA) can streamline the control of the expiring date of food, leading to less food wastage. Besides, disruptive technologies such as RPA are predicted to have an economic impact of nearly 6.7 trillion dollars by 2025. However, research on RPA or automation to manage food wastage still needs to be conducted as more needs to be done. Besides, research on the benefits and challenges needs to be done, and food wastage in Sweden is a serious issue that calls for stakeholders’ action. This research investigates the opportunities and challenges associated with using Robotic Process Automation (RPA) to reduce food wastage in Swedish grocery stores. Thus, we collected data from seven Swedish grocery store employees using semi-structured interviews. We applied thematic analysis to the collected data. The result shows several opportunities associated with using RPA in organizations for food wastage management in grocery stores. On the other hand, respondents fear losing jobs if RPA is implemented to manage food wastage.
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| 11. |
- Sandell, Johan, et al.
(författare)
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Performance Comparison Analysis of ArangoDB, MySQL, and Neo4j : An Experimental Study of Querying Connected Data
- 2024
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Ingår i: Proceedings of the 57th Annual Hawaii International Conference on System Sciences. - 9780998133171 ; , s. 7760-7769
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Konferensbidrag (refereegranskat)abstract
- Choosing and developing performant database solutions helps organizations optimize their operational practices and decision-making. Since graph data is becoming more common, it is crucial to develop and use them in big data with complex relationships with high and consistent performance. However, legacy database technologies such as MySQL are tailored to store relational databases and need to perform more complex queries to retrieve graph data. Previous research has dealt with performance aspects such as CPU and memory usage. In contrast, energy usage and temperature of the servers are lacking. Thus, this paper evaluates and compares state-of-the-art graphs and relational databases from the performance aspects to allow a more informed selection of technologies. Graph-based big data applications benefit from informed selection database technologies for data retrieval and analytics problems. The results show that Neo4j performs faster in querying connected data than MySQL and ArangoDB, and energy, CPU, and memory usage performances are reported in this paper.
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