Resource Integration Mechanisms in Self-Service Business Analytic

In a typical SSBA environment, the technical department provides data, tools and technologies specifically optimised to lower the operational complexity of processing data into information. As a result, the employees become more autonomous in fulfilling their own information needs enabling technical department to focus on more strategic tasks. In such scenario, the value of SSBA is co-created between the different actors (which is in this case business and technical employees). Co-creation occurs mainly as a result of the integration of the employee’s competencies (such as knowledge, experience and technical capabilities) with the previously mentions environment resources enabled and maintained by the technical department. As such, resource integration is considered a central activity in SSBA environment causing value generation or in other words processing data to generate business insights. Thus, through a qualitative approach, this paper aims at addressing the following research question “what mechanisms enable and facilitate resource integration in a SSBA environment”. Understanding these issues is important as more knowledge about what drives resources integration would be highly valuable for managers and IT professionals confronted by the complexity of enabling such an autonomous environment of insight generation

Patterns of Resource Integration in the Self-service Approach to Business Analytics

The main premise of Self-Service Business Analytics (SSBA) is to make business users autonomous during the data analytical process. To empower business employees, organisations are decentralising their analytical capabilities therefore adopting an SSBA approach. Yet, little is known about how employees integrate resources, such as personal competencies, environment resources including technology, and other employees’ competencies, to generate insights in SSBA. Based on the empirical data of a major Norwegian online marketplace and drawing on service-dominant logic as an analytical framework, we identify and explain two types of resource integration in an SSBA environment: direct and clustered recourse integration (including 1st tier and 2nd tier) enabled and controlled by three types of institutions. We finally discuss some organisational implications and the meaning of each sub-type of clustered resource integration

Enabling Organizational Agility Through Self-Service Business Intelligence: the case of a digital marketplace

Many organizations have adopted business intelligence and analytics systems in order to cope with the increasing digitalization of data intensive environments. In this paper, we study the role of self-service business intelligence (SSBI), a certain capability provided by a business intelligence system, in enabling organizational agility. In particular, the research question we address is as follows: How does self-service business intelligence enable organizational agility in a multi-sided platform? We focus on two types of organizational agility – namely, market capitalizing agility and operational adjustment agility – and identify how SSBI enables these capabilities in a multi-sided platform environment. We conducted 12 qualitative interviews focusing on Norway’s biggest digital marketplace, Finn.no. Our results indicate that SSBI plays an important role in enabling 1) market capitalizing agility by providing a better understanding of supply and demand participants, more access to traffic data and user clickstreams, fast response to requests, and increased access to supply and demand navigation behavior and 2) operational adjustment agility by redefining current organizational structures, empowering employees, providing equal access to organizational level data and opportunities for data manipulation. The findings provide empirical evidence for the role of SSBI in enabling organizational agility within the context of a multi-sided platform environmen

Self-Reinforcement Mechanisms of Sustainability and Continuous System Use: A Self-Service Analytics Environment Perspective

The capabilities of the people, processes, and technology are important factors to consider when exploring continuous use to create value. Multiple perceptions and attitudes towards self-service systems lead to various usage levels and outcomes. With complex analytical structures, organizations need a better understanding of IS value and users’ satisfaction. Incompatibility reduces the purpose of self-service analytics, decreasing its value and making it obsolete. In a qualitative, single case study, 20 interviews in a major digital Scandinavian marketplace were explored using the expectation–confirmation theory of continuous use to explore the mechanisms influencing the sustainability of self-service value. Two main mechanisms were identified: the personal capability reinforcement mechanism and the environment value reinforcement mechanism. This study contributes to the post-implementation and continuous use literature and self-service analytics literature and provides some practice implications to the related industry

Self-Service Business Analytics and the Path to Insights : Integrating Resources for Generating Insights

The nature of today’s business demands that Business Analytics (BA) extends to an operational level to better support employees in their decision-making. This is noticeable from the constant requests for new reports and changes in old ones at different employee levels. As a result, BA specialists or other power-users in functional departments are “bombarded” by these requests, and it becomes more of a bottleneck than ever before. This might lead inexperienced users to make critical business decisions without exploring the necessary data. SSBA addresses this need by allowing various employees at different levels across the organisation to independently build custom reports and explore previous ones without relying on the IT/BI department. As a result, the end-user role shifts from simply a consumer to a more consumer-producer role. Furthermore, organisations provide different kinds of tools and technologies for their employees to assist them in their daily decision-making. One major challenge in SSBA is that users might engage in a wrong or uneducated self-service step in their data selection or analysis, which will likely lead to wrong business decisions. Therefore, the industry needs to know how those users engage with technology and use the different resources available to generate value in terms of gaining insight from data. Also, from an academic perspective, literature on BA and DSS is abundant and covers many aspects in terms of design, implementation, use in organisations, and BA value’s speed of insight and pervasive use. However, SSBA is still under-explored, especially regarding the way resources in an SSBA environment are integrated to generate insight from data especially when employees are expected to be autonomous. Therefore, the aim of this dissertation is to explore and inform organisations about how business users develop insights in an SSBA environment. This study consists of a collection of five papers, whose findings provide answers to two research questions: RQ1— How do organisations enable an SSBA environment? And RQ2—How do users integrate resources during an analytical task in SSBA? In line with the research questions and the study’s aim, Service Dominant Logic was used as a theoretical lens. This dissertation employs an interpretive case study design to investigate SSBA. Three sources of empirical evidence have been used (semi-structured interviews, observations, and documents) to collect data from the top digital marketplace in Norway – Finn.no.From a theoretical perspective, by portraying Self-Service Business Analytics as an approach to data analytics enabled through the presence of different analytical services such as tools, technologies, and support to assist the user in achieving independence, this dissertation emphasises the central idea of a service environment and move beyond the classic description of BA and DSS. It also provides a showcase through empirical evidence on how to use S-D logic in IS research and how it could be employed as an analytical lens. Finally, this thesis contributes to both BA and S-D logic literature by theorising the resource integration patterns, modes of engagement and the self-service environment in business analytics. From a practical perspective, this thesis relates to the industry by highlighting five major points of interest in relation to information authorship, the criticality of the setup phase in SSBA, steps to solve an analytical problem, and the competencies involved

Self-Reinforcement Mechanisms of Sustainability and Continuous System Use : A Self-Service Analytics Environment Perspective

The capabilities of the people, processes, and technology are important factors to consider when exploring continuous use to create value. Multiple perceptions and attitudes towards selfservice systems lead to various usage levels and outcomes. With complex analytical structures, organizations need a better understanding of IS value and users’ satisfaction. Incompatibility reduces the purpose of self-service analytics, decreasing its value and making it obsolete. In a qualitative, single case study, 20 interviews in a major digital Scandinavian marketplace were explored using the expectation–confirmation theory of continuous use to explore the mechanisms influencing the sustainability of self-service value. Two main mechanisms were identified: the personal capability reinforcement mechanism and the environment value reinforcement mechanism. This study contributes to the post-implementation and continuous use literature and self-service analytics literature and provides some practice implications to the related industr

The Triadic Relationship of Sense-Making, Analytics, and Institutional Influences

The current business environment demands the enablement of organization-wide use of analytics to support a fact-based decision making. Such movement within the organization require employees to take advantage of the self-service business analytics tools to independently fulfil their needs. However, assuming independence in data analytics requires employees to make sense of several elements which collectively contribute to the generation of required insights. Building on sense-making, self-service business analytics, and institutions literature, this paper explores the relationship between sense-making and self-service business analytics and how institutions influence and shape such relationship. By adopting a qualitative perspective and using 22 interviews, we have empirically investigated a model developed through our literature review and provided more understanding of the sense-making concept in a self-service business analytics context

From an Information Consumer to an In-formation Author: the Role of Self-Service Business Intelligence

Self-service business intelligence (SSBI) enables executives, managers, analysts and knowledge workers to access data and build reports based on their needs to support decisions and actions toward business success. From an industrial level perspective, this suggests that business users not only consume information but that they are also able to author information. Yet, there is lack of knowledge on how SSBI extends the role of a business user beyond being an information consumer. Because SSBI falls firmly under the category of self-service technologies (SST) we draw our conclusions based on a literature review on SST. This study highlights ease of use, trust, independence, control and self-efficacy as the main characteristics that are needed to generate outcomes in terms of co-production and time efficiency at an individual level

Patterns of resource integration in the self-service approach to business analytics

The main premise of Self-Service Business Analytics (SSBA) is to make business employees autonomous during the data analytical process. To empower business employees, organizations are decentralizing their analytical capabilities through an SSBA approach. Yet, little is known about how employees integrate resources, such as, among others, personal competencies, environment resources including technology, and to generate insights in SSBA. Based on the empirical data of a major Norwegian online marketplace and drawing on service-dominant logic as an analytical framework, we identify and explain two types of resource integration in an SSBA environment: direct and clustered resource integration (including 1st tier and 2nd tier) enabled and controlled by three types of institutions. We finally discuss some organizational implications and the meaning of each sub-type of clustered resource integration

Modes of Engagement in Self- Service Business Analytics: a Service Dominant Logic Perspective

The main premise of self-service business analytics (SSBA) is to make business users autonomous during data analytics. Driven by this potential, organisations are spending resources to design SSBAs to empower business employees and decentralize the analytics capabilities. Yet, little is known about how SSBA is facilitating business employees’ independence, and moreover, the value that is co-created. Based on empirical data from a major Norwegian online marketplace and drawing on service-dominant logic as an analytical framework, we identify three main modes of data engagement in SSBA: no dependency, high dependency, and low dependency. Furthermore, we identify the required business users’ resources in the analytical processes in each mode and discuss the organisational implications of these findings