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1 IntroductionBusiness intelligence and analytics (BI&A) are data-centric approachescomplementing data with a set of methodologies, processes, technologies, andtools to analyze and extract information from data (Lim et al., 2013). BI&A offersa way for businesses to examine their data to enhance decision-making, understandtrends, and unearth valuable insights (Gürdür et al., 2019). It has evolved from datawarehousing with a focus on static reporting focus on intelligence (Simmers,2004). At the same time, it also shifts from a data transformation function into afunction of information as the focal point of the current function of datatransformation into intelligence. BI&A leverages software and services totransform data into actionable intelligence informing an organization’s strategicand tactical business decisions. With BI&A, businesses can access and analyzedata sets and present analytical findings in reports, summaries, dashboards, graphs,charts, and maps to provide users with detailed intelligence of the business’s state.In short, BI&A is an information system supporting decision-making processes byhelping organizations discover new knowledge, offer analysis solutions, ad hocqueries, reporting, and forecasting (Yoon et al., 2014).Globalization, the internationalization of markets, the knowledge economy, and ecommerce are some numerous challenges facing all organizations, regardless ofsize. If organizations will survive and be competitive in their new environment,they must use information systems (IS) and information technologies (IT) (PobaNzaou et al., 2008). Successful organizations are differentiated by their ability tomake accurate, timely, and effective decisions at all levels to address theircustomers’ preferences and priorities (Bose, 2009).The importance of small and medium-sized enterprises (SMEs) worldwide isindisputable. The definition of SMEs varies across nations; most denominators areemployment figures, turnover, and investments and fixed assets (Costello et al.,2007). This thesis follows the definition of SMEs according to the EuropeanCommission. SMEs are enterprises with fewer than 250 employees and have anannual turnover not exceeding 50 million euro, which is 99% of all European firms(IFC, 2012, p. 1). SMEs balance both political and economic independence anddrive diversified socio-economic infrastructures in the form of employment1

creation, flexibility, and innovations (Dwivedi et al. 2009). Therefore, SMEs arethe bedrock for industrialization.Unlike large enterprises, SMEs schedule a limited budget and organizationalchange (Ruivo et al., 2015), limited resources, limited expertise, and limitedimpact on their environment (Carson et al., 1995). Traditionally, SMEs are slowadopters of IS/IT (Raymond, 1988) due to scarce resources, small budgets, andlimited technical expertise. This creates potential barriers preventing them fromadopting innovative technologies to improve organizational performance (Levyand Powell, 2000). The two most important inhibitors to IS/IT progress in SMEsare financial obstacles and lack of technical knowledge (Iacovou et al., 1995).However, SMEs can be more responsive to dynamic environments and moresusceptible to digital innovations than larger enterprises (Chan et al., 2019)because of their informal structures. According to the literature, the problems,opportunities, and management issues encountered by SMEs in IS adoption areaare unique (Premkumar, 2003). In addition, their resources, capabilities, andbusiness processes are idiosyncratic in nature. Therefore, adopting BI&A in SMEslikely has different drivers and inhibitors than larger enterprises.Traditionally, the need for BI&A-driven insights might be more pronounced incompanies dealing with large amounts of information. Today, many small-scalecompanies generate a lot of data. Data generation depends on its business modelrather than firm size. Many business owners and managers are bombarded withinformation overload and urgently seek ways to derive greater control,understanding, and intelligence from organizational data. Thus, SMEs andentrepreneurs also need to make data-driven decisions.Compared to large enterprises, they lag behind in utilizing the BI&A potential(Baransel and Baransel, 2012). A BI&A initiative is not a task free from risks, nordoes it automatically achieve improved performance. Therefore, both practitionersand researchers must understand the factors influencing BI&A adoption to ensureBI&A success (Cruz-Jesus et al., 2018). Previous studies failed to presentconvincing empirical evidence of BI&A adoption in SME. Despite BI&A’ssignificance, relatively little empirical research has directly addressed the deeperunderstanding of factors influencing SME adoption processes. The business value2

generation process associated with adopting these technologies is still unclear(Moreno et al., 2018, Trieu, 2017).Given the above motivations, this thesis aims to increase the understanding ofBI&A adoption in SMEs and how SMEs create value from BI&A initiatives. Togain a better understanding, this overarching research question is investigated:How can we understand the phenomenon of BI&A adoption in SMEs? This thesisinterprets IS adoption in these stages: investment, implementation, utilization, andvalue creation from BI&A initiatives. To better understand BI&A adoption, Ispecifically investigated the following research sub-questions:SQ1: What are the drivers and inhibitors of BI&A adoption in SMEs?SQ2: How are BI&A utilized and implemented in SMEs?SQ3: How do SMEs create value from BI&A initiatives?I will answer the above questions by eliciting knowledge from BI&A experts inNorwegian industries. The empirical basis for the thesis is a ranking-type Delphistudy with a grounded Delphi approach and exploratory study using qualitativeexpert interview technique. The results of this study are presented and discussedin five research publications (Appendix B). This thesis summary will integrate theresearch publications and present the research findings coherently.The rest of this thesis is structured as follows: Chapter 2 presents the three appliedtheoretical perspectives. Chapter 3 introduces the literature of BI&A, SMEcontext, and IS adoption. Chapter 4 describes the applied research approach,including the research design, gaining informants, data collection, data analysis,and validity issues. Chapter 5 views the five research publications by summarizingeach research paper and their findings. Chapter 6 presents the thesis contributions.Chapter 7 has a thesis summary, research limitation highlights, and future researchsuggestions.3

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2 Theoretical BackgroundThis chapter will discuss the theoretical lenses applied to understand the study’sfindings. Chapter 2.1 introduces the resource-based view of the firm developed tounderstand how organizations achieve sustainable competitive advantages throughtheir resources and capabilities. The dynamic capabilities theory focuses on howorganizations develop and renew their resources and capabilities to adapt toenvironmental changes and is presented in Chapter 2.2. Finally, Chapter 2.3introduces the IS value process model to help explain how and why IS investmentsmay lead to improved organizational performance.2.1 The Resource-Based ViewThe resource-based view (RBV) traditionally emphasizes the role of resources andcapabilities as fundamental sources of firm-level value creation (Barney, 1991).RBV theorizes a firm’s resources to be a potential source of competitiveadvantage, which may improve overall performance (Wernerfelt, 1984).Wernerfelt (1984) argued this means a firm has the ability to implement a valuecreating strategy not simultaneously being implemented by current or potentialcompetitors. The RBV is an influential theoretical framework for understandinghow competitive advantage in firms is achieved and how advantage might besustained over time (Barney, 1991, Peteraf, 1993, Wernerfelt, 1995). However, theusefulness of analyzing firms from the resource perspective was not popularizeduntil the development of the RBV by Wernerfelt (1984).The extant literature has examined the types of resources capable of providingcompetitive advantage to a firm. According to Barney (1991), there are three keyRBV tenets. First, the firm’s resources are heterogeneously distributed acrossfirms, and any differences in these resources are stable. Second, there is an explicitlink between a firm’s resources, its management, and sustained competitiveadvantage. Last, there are four empirical indicators for firm resources to generatea sustained competitive advantage: valuable, rareness, inimitable, and nonsubstitutable—the so-called VRIN attributes.The RBV theory focuses on how and why some resources and capabilities arevaluable, yet rare, imperfectly imitable, and non-substitutable to allow firms toaccrue economic returns (Barney, 1996). RBV scholars argued valuable and rare5

firm resources and capabilities can attain a short-term competitive advantage. Theyalso contend these resources and capabilities must be inimitable and nonsubstitutable to produce a sustainable competitive advantage. However, eachindicator could be considered necessary, but not enough to sustain a long-termcompetitive advantage.Resources and capabilities are two terms often used without distinction. Barney(1991) classified resources into three categories: physical capital (e.g., financialassets and technology), human capital (e.g., managerial skills), and organizationalcapital (e.g., reputation, culture). Capabilities refer to a firm’s capacity to deployvalued resources either in combination or in co-presence (Schendel, 1994). Inaddition, capabilities are also firm-specific and developed over time (Barney andHansen, 1994). Competences like trustworthiness, organizational flexibility, rapidresponse to customer trends, and short product life cycles are consideredcapabilities. Based on the RBV theory definition by Wade and Hulland (2004),resources are inputs into a firm’s production process (i.e. IT equipment), whilecapabilities is a firm’s capacity to exploit IT equipment through organizationalprocesses. Through continued use of IT equipment, capabilities become moredifficult to understand and imitate by current or potential competitors. Theliterature shows a notable difference between resources and capabilities.The RBV has been applied in IS literature to explain information systems (IS) andinformation technology (IT) business value, where a firm’s resources determine itsperformance. For instance, a study by Caldeira and Ward (2003) applied RBVtheory to identify and understand factors determining the successful adoption anduse of IS/IT in (12) manufacturing SMEs. They argued several studies exploredthe applicability of RBV theory to IS/IT, mainly at a conceptual level.Recent studies applied RBV theory as a frame of reference to understand howmuch different IS/IT capabilities contribute to business value. For instance, Ruivoet al. (2015) investigated factors contributing to enterprise resource planning(ERP) value creation in SMEs. Grounded on the RBV theory, they assessed aresearch model linking three identified determinants: ERP use, collaboration, andanalytics. These explain ERP value in three effects: individual productivity,management control, and customer satisfaction. Similarly, a study byUwizeyemungu and Raymond (2012) explored the potential link between ERP6

capabilities and their contribution to organizational performance. This contributionwas conceptualized and measured through value added by automational,informational, and transformational effects of ERP capabilities on the firm’soperational and managerial processes.Similar studies have also looked at ERP from the resource-based perspective. Forinstance, a study by Laframboise and Reyes (2005) applied RBV theory to proveERP implementation influences competitive position and performance onlyindirectly through interactions with other resources. Another study by LengnickHall et al. (2004) examined ERP through the firm’s RBV theory. Their resultsproved even if ERP is necessary to coordinate complicated, multifacetedoperations, it is not enough to promote a strong, competitive long-term position.The authors argued people must change the culture to their work, the relationshipthey develop within and across firm boundaries.Viewed from the resource-based perspective, knowledge management (KM)researchers have identified different KM related resources

BA Business Analytics BI Business Intelligence BI&A Business Intelligence and Analytics CRM Customer Relationship Management CSF Critical Success Factor DBMS Database Management System D&M DeLone and McLean DOI Diffusion of Innovation Theory DSS Decision Support System DW Data Warehouse