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Business Analytics Adoption in Firms
Abstract
Business analytics leads to reliable and quicker decision making in organizations. However, few organizations can derive significant strategic value from the adoption of business analytics. Business analytics adoption is a complex organizational activity that is influenced by many factors. Therefore, the research on business analytics is of significant relevance to the industry and academic work. Many researchers have studied the adoption of business analytics in organizations using different theoretical perspectives. An analysis of the theoretical models can help in understanding the key themes for business analytics adoption and assist in future research towards extension or integration of theoretical models. Towards this end, scrutiny of research papers for business analytics adoption grounded on the theoretical perspectives of the Resource Based View (RBV), Dynamic Capabilities, and Technology-Organisational-Environmental (TOE) framework is attempted. The key themes emerging from these theoretical perspectives and the significant factors influencing analytics adoption are discussed in this conceptual paper.
... Industry Pressure (IP): The industry's burden is the impact of the kind of industry technology adoption that belongs to the company. The type of industry that a company belongs to determines the adoption of technology in an organization (Dlima et al., 2020). III. ...
... Industry Pressure (IP): The industry's burden is the impact of the kind of industry technology adoption that belongs to the company. The type of industry that a company belongs to determines the adoption of technology in an organization (Dlima et al., 2020). III. ...
Current research offers very limited insights on the process of how the adoption and continued use of cloud sourcing might trigger and push the development of business model innovation and affect the competitive advantage of a firm. Applying an abductive approach, with two longitudinal case studies of cloud sourcing firms, and a theoretical framework based on stage-based models of business model innovation and the dynamic capability view of the firm, we develop a model of stage-based business model innovation related to the adoption and continued use of cloud sourcing. The model identifies three business model innovation stages characterized by specific types of capabilities. In between the three stages, we identify three dynamic junctures that the firm and its managers have to overcome to progress from one stage to another. In the dynamic junctures three types of dynamic capabilities were key; sensing, seizing and transformation capabilities, to pass to the next stage. The model contributes to a better understanding of the evolution of dynamic capabilities as well as the evolution of the cloud sourcing firm and cloud-based business model innovation.
Abstract
The importance of big data and predictive analytics has been at the forefront of research for operations and manufacturing management. Literature has reported the influence of big data and predictive analytics for improved supply chain and operational performance, but there has been a paucity of literature regarding the role of external institutional pressures on the resources of the organization to build big data capability. To address this gap, this paper draws on the resource-based view of the firm, institutional theory and organizational culture to develop and test a model that describes the importance of resources for building capabilities, skills, and big data culture and subsequently improving cost and operational performance. We test our research hypotheses using 195 surveys, gathered using a pre-tested questionnaire. Our contribution lies in providing insights regarding the role of external pressures on the selection of resources under moderating effect of big data culture and their utilisation for capability building, and how this capability affects cost and operational performance.
Keywords: Big Data, Predictive Analytics, Institutional Theory, Resource Based View, Big Data Culture, Manufacturing Performance, PLS SEM
Purpose
The paper aims to provide a better understanding of the determinants of business intelligence system (BIS) adoption stages. It develops and empirically tests a conceptual model for assessing the determinants of BIS diffusion on the evaluation, adoption, and use stages in the context of small and medium enterprises (SMEs).
Design/methodology/approach
Drawing on data from 181 SMEs the influence of technological, organizational, and environmental factors on BIS adoption stages were analysed using the PLS-SEM method.
Findings
The paper provides empirical insights about how technological, organizational, and environmental factors affect individual BIS adoption stages.
Practical implications
The paper includes implications for managers and solution providers to understand the influence of various determinants to more effectively conclude the adoption process.
Originality/value
This study represents important progress in our theoretical understanding of the role of technological, organizational, and environmental factors across the different BIS adoption stages.
Although literature indicates that big data and predictive analytics (BDPA) convey a distinct organisational capability, little is known about their performance effects in particular contextual conditions (inter alia, national context and culture, and firm size). Grounding our investigation in the dynamic capability views and organisational culture and based on a sample of 205 Indian manufacturing organisations, we empirically investigate the effects of BDPA on social performance (SP) and environmental performance (EP) using variance based structural equation modelling (i.e. PLS). We find that BDPA has significant impact on SP/EP. However, we did not find evidence for moderating role of flexible orientation and control orientation in the links between BDPA and SP/EP. Our findings offer a more nuanced understanding of the performance implications of BDPA, thereby addressing the crucial questions of how and when BDPA can enhance social/environmental sustainability in supply chains.
Purpose
The purpose of this paper is to provide an insight about factors affecting Big Data Analytics (BDA) utilization and adoption in Indian firms. Research studies have so far focused on BDA adoption in developed economies. This study examines the factors that influence BDA usage and adoption in the context of emerging economies.
Design/methodology/approach
This study proposed a theoretical model of factors influencing BDA utilization and adoption. Two independent research streams – first, the top managers’ perceived strategic value (PSV) in BDA and second, the factors that influence the adoption of BDA theoretically – have been integrated with the technology-organization-environment (TOE) framework. In the BDA context, there was a theoretical necessity to identify the driver and barriers of BDA from the TOE framework on PSV and adoption of BDA. A qualitative exploratory study using face-to-face semi-structured interviews was carried out to collect data from 22 different enterprises and service providers in India. India was selected as the context as it is one of the fastest growing large economies of the world with huge potential of BDA to improve the business landscape.
Findings
The results showed that the major reason behind BDA non-adoption is that the organizations did not realize the strategic value (SV) of BDA, and they were not ready to make the changes because of technological, organizational and environmental difficulties. The findings corroborate previous results about significant factors affecting IT adoption and implementation and provide new and interesting insights. The main factors identified as playing a significant role in organizations’ adoption of BDA were SV of BDA, complexity, compatibility, IT assets, top management support, organization data environment, perceived costs, external pressure and industry type.
Research limitations/implications
The main limitation related to this study is the difficulty in generalizing the findings to a larger population of enterprises. To overcome this, a statistical survey has been planned to be conducted in the future.
Practical implications
The BDA adoption model in this study will have both managerial implications for practitioners in India, as well as those in other developing countries, and academic implications for researchers who are interested in BDA adoption in developing counties, in terms of formulating better strategies for BDA adoption. For managers, using the research model of this study could assist in increasing their understanding of why some organizations choose to adopt BDA, while similar ones facing similar conditions do not. Also, the understanding of the strategic utilization of BDA in different business processes may improve the adoption of BDA in organizations.
Originality/value
This paper contributes in exploring and enhancing the understanding of the factors affecting the utilization and adoption of BDA in organizations from an Indian perspective. This study is an attempt to develop and explore a BDA adoption model by the fusion of PSV and TOE framework. The effect of the three contexts of this framework (technological, organizational and environmental) on the strategic utilization of BDA has been studied for the first time.
Big data is rapidly becoming a major driver for firms seeking to gain a competitive advantage. Grounded in the Diffusion of Innovation theory (DOI), the institutional theory, and the Tech-nology–Organization–Environment (TOE) framework, this study applies the results of a content analysis to develop a framework to identify the main factors affecting the organizational adoption of big data. The content analysis is based on the retrieval and review of relevant papers in the business intelligence & analytics (BI&A) literature published during the period 2009–2015. The 26 factors identified by this review are then integrated into a TOE framework. The findings of this research enrich the current big data literature and enhance practitioners’ understanding of the decision-making processes involved in a firm’s adoption of big data.
Effective management of organizational resources in big data initiatives is of growing importance. Although academic and popular literatures contain many examples of big data initiatives, very few are repeated in the same organization. This suggests either big data delivers benefits once only per organization or senior managers are reluctant to commit resources to big data on a sustained basis. This paper makes three contributions to the Special Issue's theme of enhancing organizational resource management. One is to establish an archetype business process for big data initiatives. The second contribution directs attention to creating a dynamic capability with big data initiatives. The third identifies drawbacks of resource based theory (RBT) and it's underpinning assumptions in the context of big data. The paper discusses lessons learnt and draws out implications for practice and business research. The paper's intellectual and practical contributions are based on an in-depth case study of the European ICT Poles of Excellence (EIPE) big data initiative and evidence from the extant literature.
The era of big data has begun such that organizations in all industries have been heavily investing in big data initiatives. We know from prior studies that investments alone do not generate competitive advantage; instead, firms need to create capabilities that rival firms find hard to match. Drawing on the resource-based theory of the firm and recent work in big data, this study (1) identifies various resources that in combination build a big data analytics (BDA) capability, (2) creates an instrument to measure BDA capability of the firm, and (3) tests the relationship between BDA capability and firm performance. Results empirically validate the proposed theoretical framework of this study and provide evidence that BDA capability leads to superior firm performance.
To better understand how big data interconnects firms and customers in promoting value co-creation, we propose a theoretical framework of big data-based cooperative assets based on evidence of multiple case studies. We identify four types of big data resources and four types of associated digital platforms, and we explore how firms develop the cooperative assets by transforming big data resources via the theoretical lens of service-dominant logic. This study offers a new theoretical perspective on value co-creation and an alternative competitive strategy in the era of big data for firms.
Using extended Technology Acceptance Model (TAM), this study attempts to assess empirically crucial factors affecting cloud computing adoption intentions. It explores security, availability and compliance-related challenges in cloud computing and makes the necessary recommendation which can help to mitigate the risk in cloud computing. A framework was developed to test the effect of security, availability of cloud service provider and compliance-related factors on cloud computing adoption intentions mediated by perceived ease of use (PEOU) and perceived usefulness (PU). A questionnaire for the factors was developed to collect the data from 280 companies in information technology, manufacturing and finance sectors in India. The data was analyzed using exploratory and confirmatory factor analyses. Further, structural equation modelling using AMOS 20.0 was used to test the proposed model. The empirical results show that cloud computing adoption intention is determined by PU and PEOU. Further, PU is found to be significantly influenced by PEOU. Risk as well as availability and support are found to be significant on PEOU. In addition, threat, risk, vulnerability and compliance are found to be significant on PU. The model can be used as a guideline to ensure a positive outcome of the cloud computing adoption in organizations. The findings offer cloud computing users with a better understanding of how security, availability of cloud service provider and compliance-related factors affect cloud computing adoption intentions. It also provides relevant recommendations to achieve conducive implementation environment for cloud computing adoption. This study is an attempt to explore and develop model for cloud computing adoption intentions that was theoretically grounded in the TAM.
Despite numerous testimonials of first movers, the underlying mechanisms of organizations big data analytics (BDA) usage deserves close investigation. Our study addresses two essential research questions: (1) How does organizational BDA usage affect value creation? and (2) What are key antecedents of organizational-level BDA usage? We draw on dynamic capabilities theory to conceptualize BDA use as a unique information processing capability that brings competitive advantage to organizations. Furthermore, we employ the technology-organization-environment (TOE) framework to identify and theorize paths via which factors influence the actual usage of BDA. Survey data collected from 161 U.S.-based companies show that: organizational-level BDA usage affects organizational value creation; the degree to which BDA usage influences such creation is moderated by environmental dynamism; technological factors directly influence organizational BDA usage; and organizational and environmental factors indirectly influence organizational BDA usage through top management support. Collectively, these findings provide a theory-based understanding of the impacts and antecedents of organizational BDA usage, while also providing guidance regarding what managers should expect from usage of this rapidly emerging technology.
Business intelligence and analytics (BI&A) has emerged as an important area of study for both practitioners and researchers, reflecting the magnitude and impact of data-related problems to be solved in contemporary business organizations. This introduction to the MIS Quarterly Special Issue on Business Intelligence Research first provides a framework that identifies the evolution, applications, and emerging research areas of BI&A. BI&A 1.0, BI&A 2.0, and BI&A 3.0 are defined and described in terms of their key characteristics and capabilities. Current research in BI&A is analyzed and challenges and opportunities associated with BI&A research and education are identified. We also report a bibliometric study of critical BI&A publications, researchers, and research topics based on more than a decade of related academic and industry publications. Finally, the six articles that comprise this special issue are introduced and characterized in terms of the proposed BI&A research framework.
Purpose
– The purpose of this paper is to determine the factors influencing managers’ decision to adopt cloud computing in the UK using the “Technology-Organisation-Environment” (TOE) framework.
Design/methodology/approach
– Data were collected through a self-created questionnaire based survey that was completed by 257 mid-to-senior level decision-making business and information technology (IT) professionals from a range of UK end-user organisations. The derived hypotheses were tested using various data analysis techniques including principal component analysis and logistic regression.
Findings
– The results show that four out of the eight factors examined have a significant influence on the adoption decision of cloud computing services in the UK. Those key factors include competitive pressure, complexity, technology readiness and trading partner pressure. The latter predictor; trading partner pressure, was the most significant factor for the adoption decision of cloud services reflecting organisations’ concerns on legal regulations, co-creation and customisation, service linkage and vendor locking which adds complexity to the process of selecting an appropriate vendor.
Research limitations/implications
– This research found trading partners (cloud service providers) significantly influence managers’ decisions to adopt cloud services, however, further research is required to fully understand all the aspects involved especially with the growing number of vendors available. Although over 250 usable responses to the questionnaire were received and analysed, there was not a sufficient quantity of responses from each industry sector or organisation size to conduct further analysis.
Practical implications
– The findings reveal the important role of cloud computing service providers to enable end-users to better evaluate the use of cloud computing. It also reveals that top management support is no longer a driver as organisations are starting to adopt cloud computing services on the basis of cheaper and more agile IT resources in order to support business growth.
Originality/value
– This research provides original insight for cloud computing adoption within the UK from a managerial perspective.
Size is the first, and at times, the only dimension that leaps out at the mention of big data. This paper attempts to offer a broader definition of big data that captures its other unique and defining characteristics. The rapid evolution and adoption of big data by industry has leapfrogged the discourse to popular outlets, forcing the academic press to catch up. Academic journals in numerous disciplines, which will benefit from a relevant discussion of big data, have yet to cover the topic. This paper presents a consolidated description of big data by integrating definitions from practitioners and academics. The paper's primary focus is on the analytic methods used for big data. A particular distinguishing feature of this paper is its focus on analytics related to unstructured data, which constitute 95% of big data. This paper highlights the need to develop appropriate and efficient analytical methods to leverage massive volumes of heterogeneous data in unstructured text, audio, and video formats. This paper also reinforces the need to devise new tools for predictive analytics for structured big data. The statistical methods in practice were devised to infer from sample data. The heterogeneity, noise, and the massive size of structured big data calls for developing computationally efficient algorithms that may avoid big data pitfalls, such as spurious correlation.
The importance of big data analytics, artificial intelligence, and machine learning has been at the forefront of research for operations and supply chain management. Literature has reported the influence of big data analytics for improved operational performance, but there has been a paucity of research regarding the role of entrepreneurial orientation (EO) on the adoption of big data analytics. To address this gap, we draw on the dynamic capabilities view of firms and on contingency theory to develop and test a model that describes the role of EO on the adoption of big data analytics powered by artificial intelligence (BDA-AI) and operational performance (OP). We tested our research hypotheses using a survey of 256 responses gathered using a pre-tested questionnaire from manufacturing firms in India with the help of the National Association of Software and Services Companies (NASSCOM) and the Federation of Indian Chambers of Commerce and Industry (FICCI). The results from our analysis indicate that EO enables an organisation to exploit and further explore the BDA-AI capabilities to achieve superior OP. Further, our results provide empirical evidence based on data analysis that EO is strongly associated with higher order capabilities (such as BDA-AI) and OP under differential effects of environmental dynamism (ED). These findings extend the dynamic capability view and contingency theory to create better understanding of dynamic capabilities of the organisation while also providing theoretically grounded guidance to the managers to align their EO with their technological capabilities within their firms.
Purpose
Extant literature regarding factors essential for successful information technologies (IT) implementation in small and medium enterprises (SMEs) does not significantly address readiness factors for IT implementation in an Indian context. This exploratory research develops and tests a framework to analyse the antecedents to organisational preparedness for adoption of IT infrastructure in SMEs.
Design/methodology/approach
This exploratory research adopts a mixed-method approach to test the technology, organization and environment (TOE) framework. In-depth interviews with SME owners are conducted to develop the case study, and the measures obtained are tested through a survey at a small and medium business industrial cluster in Southern India in SMEs.
Findings
The case study indicates SME owners’ drive to initiate technology preparedness for organisational sustainability is a key factor, a measure not seen during the literature review. An empirical study tests the measures. Pressure from customers, owner’s age, sales of SME, owner’s attitude towards IT and owner’s knowledge of IT was confirmed, which indicates organisational factors have more impact compared to technological and environmental factors.
Research limitations/implications
The academic scope of this research paper can be extended to contexts such as readiness in IT infrastructure for digital transformation.
Practical implications
The validated research framework can be used by organisation stakeholders and SME IT practitioners for successful IT adoption.
Social implications
SMEs contribute significantly to gross domestic product (GDP) and provide employment opportunities. Hence, this research provides a tested model that SMEs owners/managers can adopt as a framework to augment competitiveness to implement IT.
Originality/value
The study adopts a mixed-method research design and is, perhaps, a first in the Indian context to explore variables through case study and validate identified measures through an empirical study. The model can be used by SME owners and practitioners to ascertain factors for organisational preparedness for IT adoption.
Although business analytics (BA) have been increasingly adopted into businesses, there is limited empirical research examining the drivers of each stage of BA adoption in organizations. Drawing upon technological-organizational-environmental framework and innovation diffusion process, we developed an integrative model to examine BA adoption processes and tested with 170 Korean firms. The analysis shows data-related technological characteristics derive all stages of BA adoption: initiation, adoption and assimilation. While organizational characteristics are associated with adoption and assimilation stage, only competition intensity in environmental characteristics is associated with initiation stage. Our findings help practitioners and researchers to understand what factors can enable companies to adopt BA in each stage.
Abstract: In everyday life, we come across multiple emerging applications of CloudIoT like the smartphone, smart television, smart city, smart factory, smart agriculture, etc. CloudIoT is the collaboration of cloud computing and internet of things technology. Even though IoT and cloud are having contradicting characteristics, the collaboration of IoT and cloud helps in solving their issues partially and gives rise to new services. The objective of this study is to develop a model using the structural equation modelling approach to identify the determinants of the CloudIoT technologies adoption in the Indian MSMEs, which have significant contribution in the national growth. The determinants considered for the study were cloud computing, perceived IT security risk, social influence, internet of things, perceived ease of use, perceived usefulness, trust, and intention to use. The data were collected from the 500 respondents of Indian MSMEs. The results of the investigation revealed that cloud computing,
social influence, internet of things, perceived ease of use, trust, and perceived IT security risk positively influence the intention to use. The developed model is intended to guide the organisational managers, decision, and policymakers in understanding the relationship between the various determinants and intention to use CloudIoT technologies.
Keywords: internet of things; IoT; cloud computing; CloudIoT; micro small and medium enterprises; MSMEs; structural equation modelling; SEM.
Purpose
This study aims to explore the impact of the adoption of wearable technology products for Indian health insurance firms. It identifies the key dynamic capabilities that health insurance firms should build to manage big data generated by wearable technology so as to attain a competitive advantage.
Design/methodology/approach
A qualitative exploratory study using in-depth personal interviews with 53 Indian health insurance experts was conducted with a semi-structured questionnaire. The data were coded using holistic and pattern codes and then analyzed using the content analysis technique. The findings were based on the thematic and relational intensity analysis of the codes.
Findings
An empirical model was established where all the propositions were strongly established except for the moderate relationship between wearable technology adoption and product innovation. The study established the nature of the interaction of variables on technology policy, organizational culture, strategic philosophy, product innovation, knowledge management and customer service quality with wearable technology adoption and also ascertained its influence on firm performance and competitive advantage.
Research limitations/implications
From a dynamic capabilities perspective, this study deliberates on wearable technology adoption in the health insurance context. It also explicates the relationship between the variables on technology policy, organizational culture, strategic philosophy, product innovation, knowledge management and customer service quality with wearable technology adoption on firm performance.
Originality/value
This study is one of the first studies to add the context of wearable technology and health insurance to the existing body of knowledge on dynamic capabilities and sustainable competitive advantage for the service sector. It would help existing and prospective players in adopting or setting up appropriate business models.
This chapter investigates the strategic adoption of big data (BD) and analytics (BDA) in organizations. BD represents a large and complex phenomenon which spans different disciplines. BD research is fraught with many challenges. This research develops BD adoption model that could aid organizations in assessing the strategic importance of BD to gain different advantages including gaining a competitive advantage. BD is considered a radical technology and realizing its advantages in organizations is challenged with many factors. The research attempts to outline the different aspects of BD highlighting different contributions, implications and recommendations.
This study aims at explaining the determinants of e-commerce adoption at firm level based on the technology-organisation-environment framework. The population of this explanatory research included small hotels in Bali Indonesia which had implemented e-commerce system for at least a year. The result of the analysis showed that the four variables (relative advantage, organisational readiness, management commitment and external pressure) had a significant effect to the adoption of e-commerce system, whereas statistically, the effect of the compatibility was not significant. Future researchers are expected to take more than one respondent as the informants in each of the selected hotel as the unit of the analysis. The result of this study proposed a practical implication suggesting that hotels need to consider the aspects of financial, technology, and human resources as an organisational readiness in implementing an e-commerce system.
This article sought to identify the drivers of Big Data adoption within the manufacturing and services sectors in India. A questionnaire-based survey was used to collect data from manufacturing and service sector organizations in India. The data was analyzed using exploratory and confirmatory factor analyses. Relevant hypotheses were then derived and tested by SEM analysis. The findings revealed that the following factors are important for both sectors: relative advantage, compatibility, complexity, organizational size, top management support, competitive pressure, vendor support, data management and data privacy. Statistically significant differences between the service and the manufacturing sectors were found. In other words, the relative importance of the factors for Big Data adoption differs between the sectors. The only exception was complexity, which was found to be insignificant in regard to the manufacturing sector. The factors identified can be used to facilitate Big Data adoption outcomes in organizations.
Purpose
Based on the previous literature on big data analytics (BDA) and supply chain management, this paper addresses the factors determining firms’ intention to adopt BDA in their daily operations. Specifically, this study classifies potential factors into four categories: technological, organizational, environmental factors and supply chain characteristics.
Design/methodology/approach
Drawing on the innovation diffusion theory, a model consists of direct technological and organizational factors as well as moderators was proposed. Subsequently, survey data were collected from 210 organizations. The data then were analyzed using SPSS and SmartPLS.
Findings
The empirical results revealed that perceived benefits and top management support can significantly influence the adoption intention. And environmental factors, such as competitors’ adoption, government policy, and supply chain connectivity can significantly moderate the direct relationships between driving factors and the adoption intention.
Research limitations/implications
Given the fact that big data usage in logistics and supply chain management is still in the start-up stage, the interpretations toward BDA might vary from different perspectives, thus causing some ambiguity in understanding the meaning and powerful big data has. In addition, we collect data through questionnaires completed by IT managers, which may not perfectly represent organization’s standpoint.
Practical implications
This paper tests the organizational adoption intention of big data analytics and extends the literature streams of big data and supply chain management simultaneously.
Originality/value
This paper contributes to the literature of organizational adoption intention of big data analytics and extends the literature streams of big data and supply chain management simultaneously.
Firms operating in highly competitive markets must find ways to deliver customer value beyond offering competitive prices. Providing superior customer service in such environments becomes a strategic initiative because it can create a competitive advantage by fostering customer loyalty, which can also help ease pressure on profit margins and secure continued revenue flow. In this case study we report a case of utilizing speech analytics to improve customer service quality at a call center of a pharmaceutical supply chain service provider in the U.S. We first describe the strategic rationale behind enhancing customer service quality, followed by the implementation of a quality management program using a novel approach of speech analytics. We then present a longitudinal study that evaluated customer service performance using the data gathered from a team of 120 customer service agents during an 8-month period. Two categories of key performance indicators were established and measured, namely "workforce management" metrics and "customer experience" metrics, which served as the primary indicators in the analysis of the level of success in attaining three strategically identified performance goals to improve customer service quality.
This study develops a measure for business analytics (BA) maturity and empirically examines the relationships between managerial perception of IT, BA maturity and BA success. The findings suggest that (1) BA maturity can be measured via BA integration & management support, process-level benefits of BA and technology & data analytics capabilities, (2) BA maturity positively affects organizations’ overall BA success, and (3) managerial perception of IT positively influence organizations’ achievement of BA maturity.
Advances in technology and increasing data volumes have led to new tools and techniques to exploit data and improve decision-making. Terms used to refer to these tools and techniques include big data (BD), business intelligence (BI) and business analytics (BA). Definitions of these terms are not agreed on. In particular, the terms are used as subsets or special cases of each other and this leads to confusion about their meaning and how they relate. This paper examines both academic and practitioner literature that deals with two or more of the terms to synthesise definitions and describe the relationships between them. BD is data with high volume, variety of sources and high velocity. BI is a set of tools and techniques to use data for decision-making. BA is an advanced form of BI. BD is data that can be used in both BI and BA.
This study develops and validates the concept of Data Analytics Competency as a five multidimensional formative index (i.e., data quality, bigness of data, analytical skills, domain knowledge, and tools sophistication) and empirically examines its impact on firm decision making performance (i.e., decision quality and decision efficiency). The findings based on an empirical analysis of survey data from 151 Information Technology managers and data analysts demonstrate a large, significant, positive relationship between data analytics competency and firm decision making performance. The results reveal that all dimensions of data analytics competency significantly improve decision quality. Furthermore, interestingly, all dimensions, except bigness of data, significantly increase decision efficiency. This is the first known empirical study to conceptualize, operationalize and validate the concept of data analytics competency and to study its impact on decision making performance. The validity of the data analytics competency construct as conceived and operationalized, suggests the potential for future research evaluating its relationships with possible antecedents and consequences. For practitioners, the results provide important guidelines for increasing firm decision making performance through the use of data analytics.
This paper describes a qualitative study aimed at understanding issues faced by retail firms when they start a project of implementing business analytics (BA) and understanding the impact of BA implementation on business performance. Our study is informed by prior literature and the theoretical perspectives of the Technology–Organisation–Environment (TOE) framework but is not constrained by this theory. Using case studies of nine retailers in the U.K, we have found support for the link between TOE elements and adoption. In addition, we have identified more interesting involvement of additional factors in ensuring how firms could maximise benefit derived from BA and traditional TOE factors that potentially could have additional impacts different from the ones. For example, there appears a link between adoption of BA and business performance (including performance in terms of environmental sustainability), and this link is moderated by the level of BA adoption, IT integration and trust.
This paper investigates the impact of big data and analytics (BDA) on health IT market competition as well as health IT provider’s optimal BDA adoption decisions. To capture the specific characteristics of BDA in healthcare, we simultaneously model BDA’s healthcare efficiency and privacy risk from consumer perspective and BDA’s benefit and cost from provider perspective in a stylized two-dimensional product differentiation framework. The results indicate firm’s optimal pricing strategies with the dynamic of BDA’s efficiency and privacy risk. In addition, BDA’s influence on firms’ outcomes and social welfare are analytically pointed. Theoretically, this study has potentials to provide foundations for future big data research by stylizing an analytical model to understand firm’s BDA adoption. Practically, insights for business managers on how to optimize strategies of BDA adoption, and for social planners on how to conduct better policies to improve healthcare service quality by promoting BDA adoption in healthcare, are derived.
The popularity of big data and business analytics has increased tremendously in the last decade and a key challenge for organizations is in understanding how to leverage them to create business value. However, while the literature acknowledges the importance of these topics little work has addressed them from the organization's point of view. This paper investigates the challenges faced by organizational managers seeking to become more data and information-driven in order to create value. Empirical research comprised a mixed methods approach using (1) a Delphi study with practitioners through various forums and (2) interviews with business analytics managers in three case organizations. The case studies reinforced the Delphi findings and highlighted several challenge focal areas: organizations need a clear data and analytics strategy, the right people to effect a data-driven cultural change, and to consider data and information ethics when using data for competitive advantage. Further, becoming data-driven is not merely a technical issue and demands that organizations firstly organize their business analytics departments to comprise business analysts, data scientists, and IT personnel, and secondly align that business analytics capability with their business strategy in order to tackle the analytics challenge in a systemic and joined-up way. As a result, this paper presents a business analytics ecosystem for organizations that contributes to the body of scholarly knowledge by identifying key business areas and functions to address to achieve this transformation.
The recent interest in big data has led many companies to develop big data analytics capability (BDAC) in order to enhance firm performance (FPER). However, BDAC pays off for some companies but not for others. It appears that very few have achieved a big impact through big data. To address this challenge, this study proposes a BDAC model drawing on the resource-based theory (RBT) and the entanglement view of sociomaterialism. The findings show BDAC as a hierarchical model, which consists of three primary dimensions (i.e., management, technology, and talent capability) and 11 subdimensions (i.e., planning, investment, coordination, control, connectivity, compatibility, modularity, technology management knowledge, technical knowledge, business knowledge and relational knowledge). The findings from two Delphi studies and 152 online surveys of business analysts in the U.S. confirm the value of the entanglement conceptualization of the higher-order BDAC model and its impact on FPER. The results also illuminate the significant moderating impact of analytics capability–business strategy alignment on the BDAC–FPER relationship.
Keywords
Capabilities; Entanglement view; Big data analytics; Hierarchical modeling
The amount of data produced and communicated over the Internet is significantly increasing, thereby creating challenges for the organizations that would like to reap the benefits from analyzing this massive influx of big data. This is because big data can provide unique insights into, inter alia, market trends, customer buying patterns, and maintenance cycles, as well as into ways of lowering costs and enabling more targeted business decisions. Realizing the importance of big data business analytics (BDBA), we review and classify the literature on the application of BDBA on logistics and supply chain management (LSCM) – that we define as supply chain analytics (SCA), based on the nature of analytics (descriptive, predictive, prescriptive) and the focus of the LSCM (strategy and operations). To assess the extent to which SCA is applied within LSCM, we propose a maturity framework of SCA, based on four capability levels, that is, functional, process-based, collaborative, agile SCA, and sustainable SCA. We highlight the role of SCA in LSCM and denote the use of methodologies and techniques to collect, disseminate, analyze, and use big data driven information. Furthermore, we stress the need for managers to understand BDBA and SCA as strategic assets that should be integrated across business activities to enable integrated enterprise business analytics. Finally, we outline the limitations of our study and future research directions.
Management innovation facilitates changes including technical innovation thus improving organizational performance; Chinese firms typically adopt these management innovations. Building on previous research of how and why management innovation occurs, this study measures the influence of four dynamic capabilities on four stages of the innovation process. The study surveys 264 Chinese firms and analyzes results through PLS-SEM. The findings indicate that relational capability facilitates sensing capability, absorptive capacity, and integrative capability. Further, all of these dynamic capabilities affect stages of the adoptive management innovation process; from initiation through to implementation. As innovation results can be intangible and lagging, and consequently difficult to measure, the method offers managers an alternative by monitoring the effects of different capabilities at each stage of the adoptive management innovation process.
