1,720,983 research outputs found
Digital platform ecosystem creation: a case study
No full textDigital platforms are digital contexts, where participants exchange services, information, experiences and content. They may become stable ecosystems, which can be considered viable business models in the digital economy, where value can be co-created.
Embracing the ecological metaphor, a digital platform can be considered the habitat where an ecosystem rises and expands through interactions among participants. The ecological metaphor has been applied to the field of business research, and platform ecosystems have been considered as a basic unit of analysis for studying digital economy.
In investigating digital platform ecosystems, different perspectives of analysis have been adopted by scholars.
Therefore, this paper specifically investigates the formation process of a digital platform ecosystem, exploring in a joint fashion technical features, organizational processes and business development strategies implemented, in order to identify different formation phases.
We chose the case study methodology to explore the formation of the izi.TRAVEL digital platform ecosystem in depth
The Role of Business Intelligence in Organizational Decision-making
Full text linkThis Ph.D. thesis is concerned with the role of the business intelligence (BI) output in
organizational decision-making processes. The primary focus of this thesis is to
investigate how this BI output is employed and deployed by decision-makers to shape
collective judgement and to reach organizational decisions. Concerning the role of the
BI output in decision-making the BI literature is characterized by normative ideas of
how the BI output should be used in decision-making and how it can enable people to
make better decisions. Most previous work has concerned methods and technologies to
collect, store and analyze BI. It has also, assumed a rational approach to decision
making where data from information systems are used to inform decisions either by
reducing uncertainty, ambiguity or complexity.
This study attempts to establish knowledge about the role of the BI output in the IT
project prioritization process of the Group IT of the Danske Bank Group. Hence, the
starting point of this thesis is a 16-month long interpretive study from March 2010 till
July 2011 during which I observed the prioritization process and collected various
forms of data. I use a rich dataset built from this longitudinal study of the IT project
prioritization process in Group IT where thematic analysis is used to analyze the data.
Overall, the study operates under the interpretive paradigm, which assumes that the
world and knowledge are socially constructed
Using Business Intelligence for IT Project Prioritization
Full text linkIn this paper we investigate how business intelligence can be used to support project prioritization processes in an IT development organization. The case setting is the IT development organization of a large Scandinavian financial institution where we study the relationship between business intelligence and actual organizational decisions to find ways to support organizational decisions by using business intelligence.The results show that in project portfolio choices, due to the poor business intelligence they receive, the managers make decisions based mainly on intuition and bargaining and less on business intelligence. According to the interviewed managers this leads to an inefficient process as they use a considerable amount of time in the negotiation and bargaining process, which they feel leads to suboptimal project choices. Whether this perception is justified is an open issue, as the chosen projects with rare exceptions lead to expected business value and as negotiations, e.g. based on power issues might also be necessary when ‘better information’ is available
Improving presentations of software metrics indicators using visualization techniques
No full textTo monitor and control software projects,
companies develop and invest in measurement
systems. A core component of measurement
systems are the indicators (main measurements).
Visualizing indicators can efficiently
communicate the information to the users if done
correctly, or mislead the users if not done
properly. Indicators presentation and
visualization is a topic that requires special
attention due to the overwhelming information
that the users receive and the lack of overview
solutions that drive users in missing the “big
picture”.
In this master thesis visualization techniques for
presenting indicators are evaluated. As a result
of this evaluation the most appropriate methods
for presenting indicators are identified.
Prototypes of visualizing indicators are
developed and evaluated through interviews with
engineers from a unit of a large global software
development company in the Gothenburg region.
The prototypes provide the users with four
different solutions for presenting indicators. This
study is performed at the IT University of
Göteborg with a case study at the company
IT PROJECT PRIORITIZATION – A MATTER OF INTUITION AND TRUST
Full text linkOrganizations generally have a variety of IT projects to implement, but only limited resources to develop them. As information technology and systems pervade organizations, the pool of potential IT projects is continually increasing. In this paper, we explore IT project prioritization practices in a real life context and contrast them with the rational approaches which dominate the IS literature. We present a case study conducted in a large Scandinavian financial institution in which we found that the IT project prioritization process involved an informal way of generating and collecting project ideas and that several types of constraints limited the number of projects as well the type of projects. The study shows that calculated, financial benefits are not used for prioritization, that intangible benefits are very important despite not being measured and that alternatives approaches based on intuition and trust govern the prioritization process. Our results, supported by other research studies, open up new paths for the discussion of the nature of IT project prioritization and for the improvement of a prioritization process which is based less on rational considerations and more on a balanced approach of instinct, faith and contextual reasoning which also takes tangible, calculated benefits into account. We suggest for future work a further investigation of the relationship between these elements in the context of IT project prioritization
Perceptions of Artificial Intelligence Business Value in a Value Network
No full textArtificial Intelligence (AI) continues to attract the interest of researchers and practitioners alike. Despite this interest, organisations face challenges in realising business value from AI. Studies have addressed this from either a resource and capabilities possession perspective or an action perspective. Yet, AI services are frequently provided in value networks, where stakeholders’ relationships are critical for realising value. In this study, we focus on relational aspects and investigate the stakeholders’ perceptions of the AI business value within a value network. Drawing on an empirically rich case study, we demonstrate the multifaceted and dynamic nature of AI business value. Specifically, we identify the dimensions of AI business value as expressed by the various stakeholders and show how they manifest in various configurations. The findings highlight the importance of considering the relational aspects and the partnership modes for understanding and realising AI business value and its manifestations
Towards an Understanding of Business Intelligence
Full text linkGiven the wide recognition of business intelligence (BI) over the last 20 years, we performed a literature review on the concept from a managerial perspective. We analysed 103 articles related to BI in the period 1990 to 2010. We found that BI is defined as a process, a product, and as a set of technologies, or a combination of these, which involves data, information, knowledge, decision making, related processes and technologies that support them. Our findings show that the literature focuses mostly on data and information, and less on knowledge and decision making. Moreover, in relation to the processes there is a substantial amount of literature about gathering and storing data and information, but less about analysing and using information and knowledge, and almost nothing about acting (making decisions) based on intelligence. The research literature has mainly focused on technologies and neglecting the role of the decision maker. We conclude by synthesizing a unified definition of BI and identifying possible future research streams
Using Business Intelligence in IT Governance Decision Making
No full textPart 1: GovernanceInternational audience‘Business Intelligence’ (BI) has been widely used to describe the process of gathering, analyzing and transforming large amounts of data into information useful for decision making. This paper examines BI from a decisionmaker’s perspective in an IT governance context through a case study of a large Scandinavian financial institution. The key findings indicate that BI is primarily used to inform structured operational decisions and as an instrument for dialogue in unstructured strategic decisions. Our study shows how ‘hard facts’ provided by BI are used as a foundation for opening a dialogue and as a supporting instrument to make arguments seem more convincing during decision-making discussions. We also found that standard performance reporting is used more for operational decision making, whereas predictive analytics are utilized primarily in strategic decision making. These results can assist managers looking to improve their operational and strategic decision-making processes by indicating the appropriate type of BI for each type of decision
Beyond Risk Mitigation: Practitioner Insights on Responsible AI as Value Creation
No full textWhile Responsible Artificial Intelligence (Responsible AI) has garnered much research attention, it has been approached mainly through a risk mitigation lens, focusing on downside protection and regulatory compliance. Although risk mitigation is a crucial facet of Responsible AI, it does extend beyond this as it can guide innovation towards solutions that improve lives. However, its value creation potential remains underexplored. This qualitative study addresses this gap by exploring the perspectives of AI practitioners in pioneering organisations that take a proactive, value-creation stance on Responsible AI. Thematic analysis revealed two main value creation themes: Strategic Positioning and Relational Gains. The findings of this study make an initial empirical contribution in substantiating Responsible AI practices as opportunities to create sustainable value for organisations
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