Claudio Vitari

Elisabetta Raguseo

Big data analytics business value and firm performance: Linking with environmental context

Keywords: Resource based view, contingency theory, Big Data Analytics, customer satisfaction, financial performance, market performance, munificence, dynamism

Previous studies, grounded on the resource based view, have already explored the relationship between the business value that Big Data Analytics (BDA) can bring to firm performance. However, the role played by the environmental characteristics in which companies operate has not been investigated in the literature. We inform the theory, in that direction, via the integration of the contingency theory to the resource based view theory of the firm. This original and integrative model examines the moderating influence of environmental features on the relationship between BDA business value and firm performance. The combination of survey data and secondary financial data on a representative sample of medium and large companies makes possible the statistical validation of our research model. The results offer evidence that BDA business value leads to higher firm performance, namely financial performance, market performance and customer satisfaction. More original is the demonstration that this relationship is stronger in munificent environments, while the dynamism of the environment does not have any moderating effect on the performance of BDA solutions. It means that managers working for firms in markets with a growing demand are in the best position to profit from BDA.

Introduction

Organisations are increasingly interested in the potential of big data and an increasing proportion of private and public organisations (Amankwah-Amoah, 2016) create and adopt solutions to exploit this asset [START_REF] Mcafee | Big data[END_REF]. Big Data is considered here as "the information asset characterized by such a high volume, velocity and variety to require specific technology and analytical methods for its transformation into Value" [START_REF] De Mauro | A formal definition of Big Data based on its essential features[END_REF]. As far as big data is an asset, the Resource Based View (RBV) gives a framework to the organizations for their investments in it to create value. However, big data solutions can be very diverse and they can affect value in different ways [START_REF] Ardito | A bibliometric analysis of research on Big Data analytics for business and management[END_REF]. Production domain is exemplar in this diversity, as it is at the forefront to exploit big data [START_REF] Tan | Using big data to make better decisions in the digital economy[END_REF][START_REF] Zhou | Knowledge-driven digital twin manufacturing cell towards intelligent manufacturing[END_REF]. Indeed, manufacturing is an intensive user of big data and stores more data than any other sector, such as: to discover new patterns, perform simulations, pilot industry 4.0 [START_REF] Zhou | Knowledge-driven digital twin manufacturing cell towards intelligent manufacturing[END_REF], manage complex systems in real-time [START_REF] Van Der Spoel | Predictive analytics for truck arrival time estimation: a field study at a European distribution centre[END_REF], enhance production yields [START_REF] Baily | Is Manufacturing "Cool[END_REF]A. Kumar et al., 2016), and transform supply chains [START_REF] Baryannis | Supply chain risk management and artificial intelligence: state of the art and future research directions[END_REF][START_REF] Gunasekaran | Big data and predictive analytics for supply chain and organizational performance[END_REF][START_REF] Hofmann | Big data and supply chain decisions: the impact of volume, variety and velocity properties on the bullwhip effect[END_REF][START_REF] Ivanov | The impact of digital technology and Industry 4.0 on the ripple effect and supply chain risk analytics[END_REF][START_REF] Pan | Physical Internet and interconnected logistics services: research and applications[END_REF][START_REF] Wang | Big data analytics in logistics and supply chain management: Certain investigations for research and applications[END_REF].

But not all the production activities play on the same plain big data field. Some suggest that the environmental context may have a significant role in it and contingency theory would justify it (Dale [START_REF] Stoel | IT capabilities and firm performance: A contingency analysis of the role of industry and IT capability type[END_REF][START_REF] Mariani | Online reviews: differences by submission device[END_REF][START_REF] Pratono | Strategic orientation and information technological turbulence: Contingency perspective in SMEs[END_REF]. However, research has not fully considered the effects of environmental context on the relationship between various big data solutions and performance. 1 This exploration becomes relevant if we consider that prospectively, the worldwide revenues of big data and business analytics are expected to "grow from nearly $122 billion in 2015 to more than $187 billion in 2019, an increase of more than 50% over the five-year forecast period" (IDC, 2016). Big data attracts investments because it promises to create added value in a variety of operations (OECD, 2013) and it has been identified as the "next big thing" in innovation [START_REF] Gobble | Big Data: The Next Big Thing in Innovation[END_REF]Wamba et al., 2017a). Big data benefits and risks are recognized by the firms adopting them [START_REF] Del Vecchio | Big data for open innovation in SMEs and large corporations: Trends, opportunities, and challenges[END_REF]. However, the literature shows that several Information Technologies (IT) had been announced as creating value but, once implemented in the organisations, did not actually satisfy the expectations. This bandwagon phenomenon has not yet been eradicated, even if several researchers have regularly raised alerts on this risk, for example around e-business [START_REF] Coltman | E-business: revolution, evolution or hype? Work[END_REF], green Information Systems (IS) [START_REF] Dedrick | Green IS: Concepts and issues for information systems research[END_REF] and blockchain [START_REF] Avital | Jumping on the Blockchain Bandwagon: Lessons of the Past and Outlook to the Future[END_REF]. Today, big data is highly debated and publicly promoted by policy makers and the mass media. This prominence could swamp organisations' deliberative behaviours [START_REF] Swanson | Innovating mindfully with information technology[END_REF], as happened in previous bandwagon cycles. Hence, the complex and crucial question of "Whether, when, and how to innovate with IT, confronts managers in virtually all of today's enterprises" (Swanson and Ramiller, 2004, p. 553) and could also be raised for big data initiatives. Is big data the current "me too" phenomenon?

Alerts have recently been launched to inform managers that big data is not a panacea [START_REF] Akter | How to improve firm performance using big data analytics capability and business strategy alignment?[END_REF][START_REF] Jones | What we talk about when we talk about (big) data[END_REF], such as the axiom that "an uncritical analysis of poorly understood data sets does not generate knowledge" (Matthias et al., 2017, p. 41). Multiple dangers exist like the mismanagement of inconsistent and unreliable data [START_REF] Lukoianova | Veracity roadmap: Is big data objective, truthful and credible?[END_REF], the failure of management to develop new perspective and innovative capabilities [START_REF] Akter | How to improve firm performance using big data analytics capability and business strategy alignment?[END_REF], or the inability to let data talk through "interesting and insightful questions" (Matthias et al., 2017, p. 49). Many companies have been overrun by a data-driven revolution in management [START_REF] Tambe | Big data investment, skills, and firm value[END_REF].

Hence, research is needed to face the enormous challenge of knowing how big data can be used to support decision-making (Bi et al., 2019a(Bi et al., , 2019b;;[START_REF] Li | A scientific workflow management system architecture and its scheduling based on cloud service platform for manufacturing big data analytics[END_REF][START_REF] Matthias | Making sense of Big Data-can it transform operations management?[END_REF][START_REF] Tan | Using big data to make better decisions in the digital economy[END_REF], finding a positive Return On Investment on the large investments required in this domain, which could otherwise jeopardise the entire organisation [START_REF] Braganza | Resource management in big data initiatives: Processes and dynamic capabilities[END_REF].

On one hand, a research direction that is still relatively unexplored for big data, but potentially meaningful (Côrte-Real et al., 2017), concerns the understanding of the environmental variables on business value of big data solutions and firm performance. Industry-related environmental effects are regularly recognized as possible important factors playing a moderating role on firm performances when considering the impact of IT (Dale [START_REF] Stoel | IT capabilities and firm performance: A contingency analysis of the role of industry and IT capability type[END_REF][START_REF] Li | Information technology and firm performance: Linking with environmental, strategic and managerial contexts[END_REF]. Hence, the right combination of endogenous mechanisms with external variables could help firms achieve a competitive advantage [START_REF] Burns | The Management of Innovation[END_REF]Thompson et al., 1992).

On the other hand, among the various big data studies recently focused on production needs [START_REF] Tan | Using big data to make better decisions in the digital economy[END_REF] scant attention, relative to its importance for big data, has been paid to Big Data Analytics (BDA) solutions. BDA solutions have been defined as "a holistic approach to managing, processing and analysing the 5V data-related dimensions (i.e. volume, variety, velocity, veracity and value) to create actionable insights in order to deliver sustained value, measure performance and establish competitive advantages" (Wamba et al., 2015, p. 6). This definition provides a holistic approach to the three complementary dimensions in BDA: management, technology and human [START_REF] Akter | How to improve firm performance using big data analytics capability and business strategy alignment?[END_REF]. This approach has been successfully applied in the production domain, as such, to make lean six sigma projects more effective [START_REF] Gupta | Big data in lean six sigma: a review and further research directions[END_REF], to forecast cycle time [START_REF] Wang | Big data analytics for forecasting cycle time in semiconductor wafer fabrication system[END_REF], to manage the logistics at the manufacturing shop floors [START_REF] Zhong | Big Data Analytics for Physical Internet-based intelligent manufacturing shop floors[END_REF], and at the metropolitan level [START_REF] Yang | Research on coordinated development between metropolitan economy and logistics using big data and Haken model[END_REF], and shipping in retail 4.0 (M. Kumar et al., 2016;[START_REF] Lee | A GA-based optimisation model for big data analytics supporting anticipatory shipping in Retail 4.0[END_REF]. Practitioners and academics have raised the need to continue research on BDA solutions in order to understand how, when and why BDA can be a valuable resource for organisations to gain competitive advantages [START_REF] Abbasi | Big Data Research in Information Systems: Toward an Inclusive Research Agenda[END_REF][START_REF] Agarwal | Big data, data science, and analytics: The opportunity and challenge for IS research[END_REF]Côrte-Real et al., 2017;[START_REF] Erevelles | Big Data consumer analytics and the transformation of marketing[END_REF][START_REF] Lavalle | Big data, analytics and the path from insights to value[END_REF][START_REF] Xu | Effects of big data analytics and traditional marketing analytics on new product success: A knowledge fusion perspective[END_REF]. This is specifically true for production research, as far as the BDA can be helpful to support global manufacturing and supply chain innovation by creating data transparency, improving human decision-making and promoting innovative business models [START_REF] Manyika | Big Data: The next frontier for innovation, competition, and productivity[END_REF][START_REF] Tan | Using big data to make better decisions in the digital economy[END_REF].

Based on these considerations, our original contribution is centred on the inclusion of the environmental variables, such as environmental dynamism and environmental munificence, in the understanding of the impact of BDA on firm performance. The aim of this study was thus to address the following research question: To what extent do environmental dynamism and environmental munificence moderate the effect of the business value of BDA solutions on the performance of a firm? The combination of survey data and secondary financial data on a representative sample of medium and large companies will allows to answering our research question.

The rest of the paper is structured as follows. First, we present the theoretical background from a RBV perspective, and formulate our hypotheses. We then detail the methodology that we followed and present our results. We continue with a discussion of the findings, our conclusions and guidelines for future studies.

Theoretical foundations and research hypotheses

Interest in assessing business value and firm performance of BDA solutions is increasing (Akter et al., 2016;[START_REF] Kamble | Big data-driven supply chain performance measurement system: a review and framework for implementation[END_REF][START_REF] Kiron | The analytics mandate[END_REF][START_REF] Mcafee | Big data[END_REF]. Initial results put forward that these analytics solutions could be the critical elements that are needed to transform overwhelming data into business value and ultimately into business performance. "BDA is now considered a game changer that can enable improved business efficiency and effectiveness because of its high operational and strategic potential" (Wamba et al., 2017a). Several studies have highlighted the effects of BDA business value on firm performance (Ji-fan [START_REF] Ren | Modelling quality dynamics, business value and firm performance in a big data analytics environment[END_REF][START_REF] Raguseo | Investments in big data analytics and firm performance: an empirical investigation of direct and mediating effects[END_REF]. At the same time, practitioners and academics have raised the need to continue research in order to understand how, when and why BDA can be a valuable resource for organisations to gain competitive advantages [START_REF] Abbasi | Big Data Research in Information Systems: Toward an Inclusive Research Agenda[END_REF][START_REF] Agarwal | Big data, data science, and analytics: The opportunity and challenge for IS research[END_REF]Côrte-Real et al., 2017b;[START_REF] Erevelles | Big Data consumer analytics and the transformation of marketing[END_REF][START_REF] Lavalle | Big data, analytics and the path from insights to value[END_REF][START_REF] Xu | Effects of big data analytics and traditional marketing analytics on new product success: A knowledge fusion perspective[END_REF]. The integration of the resource based view theory of the firm with the contingency theory aims to this understanding.

Resource based view

The RBV theory of the firm can explain the extent to which BDA solutions contribute to the creation of a competitive edge (Wamba et al., 2017a). A firm obtains a competitive edge when it enjoys greater success than its competitors [START_REF] Davenport | Competing on analytics[END_REF][START_REF] Peteraf | Unraveling the resource-based tangle[END_REF]. In the RBV, it is important to distinguish business value from firm performance (Ji-fan [START_REF] Ren | Modelling quality dynamics, business value and firm performance in a big data analytics environment[END_REF]. Business value is the central construct of the RBV and it stands between the rare, inimitable and non-substitutable resources of the firm and the performance of the firm [START_REF] Kozlenkova | Resource-based theory in marketing[END_REF][START_REF] Melville | Review: Information technology and organizational performance: An integrative model of IT business value[END_REF].

In line with this stream of research grounded on the RBV, we propose that BDA solutions could generate, firstly, a higher business value, and, subsequently, a higher firm performance. The emergence of the business value dimension for big data is relatively recent in time. At the beginning, big data was characterised by three Vs: "high-Volume, high-Velocity and/or high-Variety" (Gartner, 2012). The fourth V, Veracity [START_REF] Lukoianova | Veracity roadmap: Is big data objective, truthful and credible?[END_REF], and fifth V, Value [START_REF] Wamba | How 'big data'can make big impact: Findings from a systematic review and a longitudinal case study[END_REF], were theorized later on. Including the five V dimensions of big data in the RBV means that big data are a rare, inimitable and non-substitutable information asset characterized by their high Volume, high Velocity, high Variety, and uncertain Veracity. The business Value extracted from this asset completes the four other Vs of big data as information asset. Unfortunately, extracting business value from this information asset is the most critical problem due to the intrinsic complexity of data characterized by high Volume, high Velocity, high Variety, and uncertain Veracity [START_REF] Chen | Big data: a survey[END_REF].

Effect of the business value of BDA solutions on firm performance

The effect of the business value of BDA solutions on firm performance depends on the specific big data solutions that the organisations set up, in relation to their scale, their time horizon [START_REF] Matthias | Making sense of Big Data-can it transform operations management?[END_REF] and nature (G. Wang et al., 2016;[START_REF] Wang | Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations[END_REF]. Concerning their scale, a big data application can have a narrow scale, covering a single operation of a business process, such as product suggestions, while another application can have a wider scope, covering entire business domains, such as a whole supply chain (G. Wang et al., 2016). Concerning their time horizon and nature, a big data application can have a past, hence descriptive, orientation, such as in auditing solutions. Otherwise a big data application can have a present and predictive orientation [START_REF] Lee | A GA-based optimisation model for big data analytics supporting anticipatory shipping in Retail 4.0[END_REF][START_REF] Priya | A novel intelligent approach for predicting atherosclerotic individuals from big data for healthcare[END_REF][START_REF] Van Der Spoel | Predictive analytics for truck arrival time estimation: a field study at a European distribution centre[END_REF], such as real-time trading tools. Finally, a big data application can have a future, hence prescriptive (Amankwah-Amoah, 2016), orientation for example in strategic decision-support systems (Bi et al., 2019a(Bi et al., , 2019b;;[START_REF] Gunasekaran | Big data and predictive analytics for supply chain and organizational performance[END_REF]. The various possible combinations explain why BDA solutions could potentially provide business value in the most diverse activities of any organisation [START_REF] Tan | Harvesting big data to enhance supply chain innovation capabilities: An analytic infrastructure based on deduction graph[END_REF][START_REF] Wamba | How 'big data'can make big impact: Findings from a systematic review and a longitudinal case study[END_REF][START_REF] Wang | Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations[END_REF].

Given this diversity in the applicability of big data, we look at firm performance, by taking into consideration the financial performance, the market performance and the customer satisfaction.

Two studies (Ji-fan [START_REF] Ren | Modelling quality dynamics, business value and firm performance in a big data analytics environment[END_REF][START_REF] Raguseo | Investments in big data analytics and firm performance: an empirical investigation of direct and mediating effects[END_REF] have already looked at the relationship between the business value of BDA solutions and firm performance, employing to different extent these three dimensions of firm performance. The first (Ji-fan [START_REF] Ren | Modelling quality dynamics, business value and firm performance in a big data analytics environment[END_REF] considered firm performance as composed of two dimensions: financial and market performance. Financial performance referred to revenue growth and profitability, while market performance was about improving a firm's position against its competitors [START_REF] Mithas | How information management capability influences firm performance[END_REF][START_REF] Tippins | IT competency and firm performance: is organizational learning a missing link?[END_REF]. The second [START_REF] Raguseo | Investments in big data analytics and firm performance: an empirical investigation of direct and mediating effects[END_REF] added customer satisfaction into the equation and justified that customer satisfaction and market performance are mediating variables between the business value of BDA solutions and financial performance. The results of these studies suggested that the business value of BDA solutions has an impact on the performance of a firm, with no mediating effects, when firm performance is measured as composed of financial and market performance (Ji-fan [START_REF] Ren | Modelling quality dynamics, business value and firm performance in a big data analytics environment[END_REF], and with customer satisfaction and market performance as mediating variables when exclusively financial performance is the dependent variable [START_REF] Raguseo | Investments in big data analytics and firm performance: an empirical investigation of direct and mediating effects[END_REF]. As a consequence, our dependent variable, firm performance, includes these three dimensions: financial performance, market performance and customer satisfaction, defined as followed.

The financial performance of a firm is a commonly examined dependent variable measuring the competitive advantage of a company [START_REF] Kaufman | The RBV theory foundation of strategic HRM: critical flaws, problems for research and practice, and an alternative economics paradigm[END_REF]. Initial evidence emerging from the literature attest of the opportunities, through BDA solutions, to greatly improve financial performance [START_REF] Akter | How to improve firm performance using big data analytics capability and business strategy alignment?[END_REF][START_REF] Wamba | How 'big data'can make big impact: Findings from a systematic review and a longitudinal case study[END_REF]Wamba et al., , 2017a)). Results show that BDA solutions can improve Return On Investment for retailers (Wamba et al., 2017a), procurement processes [START_REF] Bock | A new two-dimensional performance measure in purchase order sizing[END_REF], or ecommerce purchasing process completion [START_REF] Jayanand | Big data computing strategies[END_REF].

Market performance refers to the organisation's ability to have higher market shares, to enter new markets more rapidly, to introduce new products and services more frequently, and to have higher product and service success rates, than its competitors. Scholars have already advanced that big data can be incorporated in marketing and new product development [START_REF] Tan | Harvesting big data to enhance supply chain innovation capabilities: An analytic infrastructure based on deduction graph[END_REF][START_REF] Wamba | How 'big data'can make big impact: Findings from a systematic review and a longitudinal case study[END_REF]Wamba et al., , 2017a;;[START_REF] Xu | Effects of big data analytics and traditional marketing analytics on new product success: A knowledge fusion perspective[END_REF]. BDA solutions would facilitate the recognition of market opportunities and threads and define the best market, product and service strategies through a data lens [START_REF] Brands | Big Data and Business Intelligence for Management Accountants[END_REF]Côrte-Real et al., 2017;[START_REF] Davenport | Big data at work: dispelling the myths, uncovering the opportunities[END_REF], via for example a better customer segmentation [START_REF] Wamba | How 'big data'can make big impact: Findings from a systematic review and a longitudinal case study[END_REF]. BDA solutions could also open to new kinds of commercial offers that leverage the digitalisation processes, being potentially disruptive of the traditional business models and generating new revenue streams by selling information complementarily to the traditional product and service offers [START_REF] Opresnik | The value of Big Data in servitization[END_REF].

Customer satisfaction is a function of how goods and services meet or surpass the expectations of customers. In general, a customer compares the perceived performance of a product with her performance standard. The customer is satisfied when her perceived performance is greater than her performance standard, and dissatisfied when her performance falls short. BDA solutions could improve how customers understand and exploit this better knowledge to increase customer satisfaction (Wamba et al., 2017b), decrease customer acquisition costs [START_REF] Liu | Big data and predictive business analytics[END_REF], strengthen the customer relationship [START_REF] Cheng | Big data analytics with swarm intelligence[END_REF], improve customization [START_REF] Wamba | How 'big data'can make big impact: Findings from a systematic review and a longitudinal case study[END_REF], and improve overall customer experience [START_REF] Tan | Harvesting big data to enhance supply chain innovation capabilities: An analytic infrastructure based on deduction graph[END_REF][START_REF] Tweney | Walmart scoops up Inkiru to bolster its 'big data'capabilities online[END_REF].

Contingency theory

The role played by the environmental context, in the relationship between BDA value and firm performance, has not yet been investigated. To understand this aspect, we propose to lever the contingency theory and to integrate this theory to the RBV perspective.

The contingency theory advances that organizational effectiveness results from fitting the characteristics of the organisation to the contingencies that reflect the situation of the organisation (Dale [START_REF] Stoel | IT capabilities and firm performance: A contingency analysis of the role of industry and IT capability type[END_REF][START_REF] Donaldson | The contingency theory of organizations[END_REF]: the better the fit, the higher the organisational performance. Hence, contingency theory has the intention to understand how firms align their expected performance with both the internal and external business environment [START_REF] Homburg | Marketing performance measurement systems: does comprehensiveness really improve performance?[END_REF]. Moreover, the attaining of the fit is a continuous seek due to the changing contingencies over time.

The external environment is one of the first and most important identified contingencies [START_REF] Burns | The management of innovation[END_REF], followed by strategy and organizational size [START_REF] Child | Managerial and organizational factors associated with company performance-part II. A contingency analysis[END_REF]. Hence, organizations should not only acquire and develop their resources, as advanced by the RBV [START_REF] Barney | Firm resources and sustained competitive advantage[END_REF], but they also should enhance the capability to deal with environmental contingencies. The right combination of endogenous mechanisms with external variables could help firms achieving a competitive advantage [START_REF] Burns | The Management of Innovation[END_REF]Thompson et al., 1992).

In line with the contingency theory development, we propose that the environmental variables could influence the organisational alignment, moderating the relationship of the BDA value on firm performance. A moderator is a variable that affects the direction and/or strength of the relationship between an independent and a dependent variable (Baron and Kenny, 1986). Even though, big data are rare, inimitable and non-substitutable information assets, extracting business value from this information asset would be moderated by the environmental contingencies. Industry-related environmental effects are regularly recognized as possible important factors playing a moderating role on firm performances when considering the impact of Information Technology (IT) [START_REF] Li | Information technology and firm performance: Linking with environmental, strategic and managerial contexts[END_REF].

More specifically, we consider the levels of munificence and dynamism in the environment where firms do business. These moderators are used extensively in Information Systems (IS) studies (e.g., Venkatesh et al., 2012), as the external challenges that firms have to face.

Moderating effect of the environmental dynamism

Environmental dynamism appears to constitute a critical dimension of a firm's exogenous environment. Environmental dynamism refers to the rate of instability in an industry, which could concern changes in customer preferences and/or competitor strategies (Dale [START_REF] Stoel | IT capabilities and firm performance: A contingency analysis of the role of industry and IT capability type[END_REF]. The contingency theory recognizes that Environmental dynamism can have the power to moderate business performance (Lumpkin and Dess, 2001;Teece et al., 1997) and it constitutes a central factor explaining the degree of success in the development of organisational resources [START_REF] Simerly | Environmental dynamism, capital structure and performance: a theoretical integration and an empirical test[END_REF][START_REF] Wu | Applicability of the resource-based and dynamic-capability views under environmental volatility[END_REF]. In practice, contingency theory contributes to the explanation of the accentuated managerial risks existing in industries that are highly dynamic. The managers experience much more uncertainty and have only little pieces of doubtful information. Moreover, possible developments and strategic options are not clearly visible to the firm. These risks and this lack of visibility can potential impact the firm ability to convert value into performance (Rösmann et al., 2017).

The contingency theory highlight that, in these dynamic environments, response time is particularly important (Bechor et al., 2010). In this context, investments in IT may serve as an effective way to provide timely and relevant information to upper managers and thus to reduce levels of uncertainty (Li and Richard Ye, 1999). When firms are slow to respond, they may miss opportunities or be preempted by competitors (Bhatt et al., 2010). Conversely, firms that respond quickly to customer changes or competitor moves may often realise long-term performance benefits.

Thanks to the integration of the RBV theory and the contingency theory, we expect that firms able to generate higher business value from their BDA solutions can achieve better firm performance in dynamic environments. For example, a present orientation in BDA solution could be leveraged based on real-time reactions, and accentuates the organizational agility (Côrte-Real et al., 2017;[START_REF] Wang | Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations[END_REF]. BDA solutions may provide organisations with insight into customers' expressed and latent needs, and the velocity dimension of the big data may be a key to transform these insights into better customer satisfaction, higher market performance and stronger financial results [START_REF] Hofmann | Big data and supply chain decisions: the impact of volume, variety and velocity properties on the bullwhip effect[END_REF].

Based on these considerations, we expect that firms that develop high levels of BDA solutions achieve higher levels of firm performance under high levels of environmental dynamism. This grounds our first set of hypotheses: H1: The higher the level of environmental dynamism, the higher the contribution of BDA solutions will be to firm performance, in terms of (H1a) financial performance, (H1b) market performance, and (H1c) customer satisfaction.

Moderating effect of environmental munificence

The contingency theory advances that environmental munificence could be an equally important dimension that should be taken into account. Munificence refers to the extent to which opportunities exist and the degree to which an environment makes resources available to sustain growth (Dale [START_REF] Stoel | IT capabilities and firm performance: A contingency analysis of the role of industry and IT capability type[END_REF]Rosenbusch et al., 2013). Munificent environments are characterised by growth in customer demands; thus, firms must be prompt in responding to growing customer needs (Xue et al., 2012). Environmental munificence also enhances the value of the organisational resources that promote low operating costs [START_REF] Terjesen | Alliance diversity, environmental context and the value of manufacturing capabilities among new high technology ventures[END_REF].

The contingency theory would support also that munificent environments could extend the potential of BDA solutions in experimentation and innovation [START_REF] Tan | Harvesting big data to enhance supply chain innovation capabilities: An analytic infrastructure based on deduction graph[END_REF][START_REF] Wamba | How 'big data'can make big impact: Findings from a systematic review and a longitudinal case study[END_REF] and their transformation in firm performance. A future orientation of a firm's BDA solution [START_REF] Gunasekaran | Big data and predictive analytics for supply chain and organizational performance[END_REF] could generate higher benefits when a new forward looking strategic initiative matches with a contingent environment with a growing demand. The BDA solutions could facilitate, for example in the fashion industry, the suppliers to perceive where and when a specific style of clothing may become the top seller (Dale [START_REF] Stoel | IT capabilities and firm performance: A contingency analysis of the role of industry and IT capability type[END_REF]. Thus, the teachings of the contingency theory would support the proposition that the value adding potential of superior BDA solutions is likely to be more pronounced in highly munificent environments. Finally, the analysis of big data may support more timely interactions with new opportunities (e.g., proposing new offers to customers). Such interactions may in turn reveal a variety of avenues for business expansion and profit. In short, the integration of the RBV theory and the contingency theory would raise expectations that firm performance resulting from BDA solutions would be more pronounced in highly munificent environments. This establishes the ground of our second set of hypotheses: H2: The higher the level of environmental munificence, the higher the contribution of BDA solutions will be to firm performance, in terms of (H2a) financial performance, (H2b) market performance, and (H2c) customer satisfaction.

Overall, we test the research model shown in Figure 1 by drawing on RBV and contingency theory. We argue that the business value derived from the use of BDA solutions has an impact on the firm performance, through the contribution of two moderating variables: environmental munificence and environmental dynamism.

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Data collection

We administered our questionnaire to medium-and large-sized French firms to evaluate the impact of the BDA business value on the firm performance. As our study considers the effects at the firm level, we followed previous studies that targeted the Chief Information Officer (CIO) as the main informant. We implemented a random sampling method to select medium and large French companies to interview from a population of 19,875 medium and large companies belonging to the sectors shown in Table 1 and registered in the Bureau Van Dijk's DIANE database, which is one of the main sources of financial information on firms in France. We aimed to gather 200 questionnaires, assuring a 95% confidence level and a 6.9% confidence interval in representing the whole starting population. Firms were categorized by size based on their revenues, accordingly to the European definition, where small companies generate less than 10€ million, medium-sized firms between 10€ and 50€ million, and large companies more than 50€ million. We conducted a pilot study with 30 companies, contacting a sample of 142 companies (response rate of 21.13%), to test the comprehensibility of the questions, to identify possible response issues, to establish the expected response rate and hence the sampling needs. All the questions led to appropriate answers and thus did not require further changes. Therefore, the final questionnaire remained unchanged. To attain our target of 200 valid questionnaires, we looked for 170 additional valid questionnaires, beyond the 30 valid questionnaires gathered for the pilot. Our search for 170 additional valid questionnaires brought us to contact a sample of 1,962 additional companies (response rate of 8.66%). The data gathering process involved three steps. In the first step, we contacted the company to inform them about the aim of the research study and to ask permission to contact the CIO. In the second step, the CIO was contacted and asked about his/her willingness to participate in the survey. When the CIO was not available at the time agreed upon in the first call, we made a second appointment. Therefore, the questionnaire was completed, either in the second or third step, according to the availability of the CIO. When the CIO was unable to answer the questionnaire, we identified another qualified respondent knowledgeable about the firm's investments and the adoption of BDA solutions.

------Table 1. Sample characteristics

Measures

The questionnaire consisted of two sections. The first section, which all companies answered, assessed the presence or absence of BDA solutions. This first section included questions to triangulate the presence of big data in its three founding dimensions: Velocity, Variety and Volume. The following question was included for Velocity: Up to now, what is the shortest latency of your data? Respondents had a single choice possibility, among the following alternatives:  Less than 1 Terabyte,  Between 1 Terabyte and 1 Petabyte,  Between 1 Petabyte and 1 Exabyte,  Between 1 Exabyte and 1 Zettabyte,  More than 1 Zettabyte In search for big data, we set a threshold for each question. As far as Velocity was concerned, we looked for real-time or near-real-time latency. The presence of more than one data source was assessed to establish Variety. As for Volume, we asked whether the size of the stored data exceeded a Petabyte. If, at least, one response passed its respective threshold, we deduced that the organisation could have a BDA solution. Hence we explicitly asked the respondents to confirm our deduction that the organisation has BDA solutions. Only if the respondent explicitly confirmed our deduction, the company was asked to fill in the second section of the questionnaire.

In the second section, we checked once again whether the company had at least one BDA solution, by asking about the kinds of BDA solutions the company had, among the following list, offering a multiple choice possibility:

1. Visual analytics software or other software used to display analytical results in visual formats.

2. Scripting languages or other programming languages that work well with big data (e.g., Python, Pig, and Hive).

3. In-memory analytics software or other processing big data used in computers for greater speed.

4. MapReduce and Hadoop software or other software used to process big data across multiple parallel servers.

5. Machine learning software or other software used to rapidly find the model that best fits a data set.

6. Natural language processing or other software used for texts -information extraction, text summarization, question answering, or sentiment analysis.

7. Social media analytics software (content-based analytics and structure-based analytics).

8. Predictive analytics software used to extract information from data and predict trends and behaviour patterns.

Beyond this double check question, the second section assessed the dependent and independent variables of our empirical model: firm performance and BDA business value (Table 2 and Figure 2).

Dependent variable

Firm performance. It is defined as the financial performance, market performance and customer satisfaction of the organisation (Ji-fan [START_REF] Ren | Modelling quality dynamics, business value and firm performance in a big data analytics environment[END_REF][START_REF] Raguseo | Investments in big data analytics and firm performance: an empirical investigation of direct and mediating effects[END_REF]. Financial performance refers to the firm's ability to improve profitability and return on investment. It was assessed using three items based on a seven-point Likert scale, with responses ranging from "completely disagree" (-3) to "completely agree" (+3) (Ji-fan [START_REF] Ren | Modelling quality dynamics, business value and firm performance in a big data analytics environment[END_REF][START_REF] Mithas | How information management capability influences firm performance[END_REF]. Market performance refers to the firm's ability to gain and retain customers. It was assessed using four items based on a seven-point Likert scale, with responses ranging from "completely disagree" (-3) to "completely agree" (+3) (Ji-fan [START_REF] Ren | Modelling quality dynamics, business value and firm performance in a big data analytics environment[END_REF]. Customer satisfaction refers to the firm's ability to meet or surpass customer expectations. It was assessed using four items based on a sevenpoint Likert scale, with responses ranging from "completely disagree" (-3) to "completely agree" (+3) [START_REF] Mithas | How information management capability influences firm performance[END_REF][START_REF] Vorhies | Benchmarking marketing capabilities for sustainable competitive advantage[END_REF].

Independent variable

BDA business value. It is defined as the transactional, strategic, transformational and informational value of the BDA solution (Ji-fan [START_REF] Ren | Modelling quality dynamics, business value and firm performance in a big data analytics environment[END_REF]. It is the combination of four sub dimensions and it is operationalized as a second-order variable (Figure 2). The first, transactional value refers to the degree to which the user perceives that BDA solutions provide operational benefits as reducing operating costs or communication costs. It was assessed using four items based on a seven-point Likert scale, with responses ranging from "completely disagree" (-3) to "completely agree" (+3) [START_REF] Gregor | The transformational dimension in the realization of business value from information technology[END_REF][START_REF] Ren | Modelling quality dynamics, business value and firm performance in a big data analytics environment[END_REF].

The second, strategic value, refers to the degree of perceived benefits for the organization at the strategic level, as enabling quicker response to change or improving customer relations [START_REF] Centobelli | Managing customer knowledge through the use of big data analytics in tourism research[END_REF]. It was assessed using three items based on a seven-point Likert scale, with responses ranging from "completely disagree" (-3) to "completely agree" (+3) [START_REF] Gregor | The transformational dimension in the realization of business value from information technology[END_REF][START_REF] Ren | Modelling quality dynamics, business value and firm performance in a big data analytics environment[END_REF]. The third, transformational value, refers to the degree of perceived changes in the structure and capacity of a firm as a result of BDA solutions, which serve as a catalyst for future benefits. It was assessed using four items based on a seven-point Likert scale, with responses ranging from "completely disagree" (-3) to "completely agree" (+3) [START_REF] Gregor | The transformational dimension in the realization of business value from information technology[END_REF][START_REF] Ren | Modelling quality dynamics, business value and firm performance in a big data analytics environment[END_REF].

The fourth, informational value, refers to the degree to which the user of BDA solutions benefits from better information as improving the management of data or enabling faster access to data. It was assessed using three items based on a seven-point Likert scale, with responses ranging from "completely disagree" (-3) to "completely agree" (+3) [START_REF] Gregor | The transformational dimension in the realization of business value from information technology[END_REF][START_REF] Ren | Modelling quality dynamics, business value and firm performance in a big data analytics environment[END_REF].

Moderating variables

The moderating and control variables have been assessed using DIANE Bureau Van Dijk databases (Table 3).

Environmental dynamism. Environmental dynamism is the rate of instability of the environment, which is the result of changes in the customer preferences, the development of new products, new technology, or the competition (Dale [START_REF] Stoel | IT capabilities and firm performance: A contingency analysis of the role of industry and IT capability type[END_REF]. To operationalize this environmental contingency, we levered the approach of Dess and Beard (1984). Following Dale [START_REF] Stoel | IT capabilities and firm performance: A contingency analysis of the role of industry and IT capability type[END_REF], we measured environmental dynamism as variability in annual industry sales. Specifically, it was assessed using DIANE Bureau Van Dijk databases, which contain firm and industry data defined at the three-digit Standard Industrial Classification (SIC) industry level (Johnson and Greening, 1999). For each sector, industry-level total sales for five years (from 2010 to 2015) were regressed on the year variable. Dynamism was measured as the standard error of the regression slope coefficient of annual industry sales divided by the industry mean for the five-year period.

Environmental munificence. It refers to the extent to which the environment can support sustained growth. Industries that are mature or shrinking are characterized as having low munificence and competition is intense, leading to price wars, which give low-cost producers a distinctive advantage (Dale [START_REF] Stoel | IT capabilities and firm performance: A contingency analysis of the role of industry and IT capability type[END_REF]. Industry munificence was assessed using the DIANE Bureau Van Dijk databases. Based on the fact that munificent environments are those which support sustained growth, using data on total industry sales revenues, environmental munificence was measured as the growth rate in annual industry sales over five years (from 2010 to 2015), as measured by the regression slope coefficient divided by average industry sales (Dale [START_REF] Stoel | IT capabilities and firm performance: A contingency analysis of the role of industry and IT capability type[END_REF].

Control variables

Firm size. We operationalized the firm size with the logarithmic form of the sales of every company.

Firm age. We operationalized the firm age with the logarithmic form of the firm age by considering the foundation year.

Industries. We operationalized the firm's industry by generating a set of dummy variables, one for each sector. To simplify the readability of the models, we omitted the coefficients of these variables in the Table 7, showing the regression results. Industry code was based on the 1-digit Standard Industry Classification (SIC) level.

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The empirical models

The hypotheses were tested using the dataset of companies that use BDA (38% of companies surveyed), out of the 200 companies received. We tested the moderating effects of the two moderating variables, environmental dynamism and environmental munificence, on the relationship between the business value of big data and firm performance. The independent variables were standardized, since we included the interaction variable, for evaluating the moderation effects in the models.

We have addressed the concern of the reverse causality and the endogeneity of IT investment (Lee et al., 1997;Brynjolfsson and Hitt, 2000;Aral et al., 2006) using instrumental variables (IV) techniques. Specifically, we used a two-stage least-squares (2SLS) regression approach. Thus, we used instruments that must be correlated with BDA business value but not with dependent variables. We employed three IV: the big data Variety, the big data Volume and the big data Velocity questions, available from the first section of the questionnaire, as explained before.

Overall, we run twelve 2SLS models with the IV to verify the moderating effects. X t is the set of control variables that could influence the performance of a company and each model includes the logarithm of the firm size and the industry dummy variables. These twelve models can be grouped in four subsets.

The first subset refers to the direct effects of the independent variables, considering the moderating variables as independent, on the three dependent variables. The first three models, Model 1 to Model 3, contain as independent variables the control variables and the three first order variables (BDA business value, environmental dynamism and environmental munificence), and as dependent variables respectively financial performance (Model 1), market performance (Model 2) and customer satisfaction (Model 3). They take the following forms: The second subset, Model 4 to Model 6, refers to the moderating effect of environmental dynamism on the relationship between the BDA business value and every firm performance investigated. They differ from the previous three model to the extent that they contain as independent variable the interaction effects between the BDA business value variable and the environmental dynamism variable. They take the following forms: The third subset, Model 7 to Model 9, refers to the moderating effect of environmental munificence on the relationship between the BDA business value and every firm performance investigated. They are different to the previous ones, as they contain as independent variable the interaction effects between the BDA business value variable and the environmental munificence variable. They take the following forms:

Analyses and results

Psychometric properties of the measures

Before running the regressions, we conducted a Confirmatory Factor Analysis in order to verify whether the variables already used in other studies have the appropriate psychometric properties of the measures investigated in this study (Table 4). The loadings of the measures on their respective constructs ranged from 0.672 to 0.880. We consider these loadings satisfactory [START_REF] Hair | Multivariate data analysis[END_REF].

The t-statistic of each factor loading was compounded to verify convergent validity. All the factor loadings were found to be statistically significant, and all the t-values were higher than the cut-off point of 1.980. The overall constructs were meritorious, given that the Kaiser-Meyer-Olkin measure of the sampling adequacy was equal to 0.843 and that Bartlett's test of sphericity gave a statistically significant chi-square value of 1,156 (p-value = 0.001). The recommended reliability levels and Average Variance Extracted (AVE) were also observed. Cronbach's alpha values ranged from 0.650 to 0.839, and the AVE values ranged from 0.516 to 0.676. These values are higher than the acceptability threshold values [START_REF] Bagozzi | On the evaluation of structural equation models[END_REF][START_REF] Churchill | A paradigm for developing better measures of marketing constructs[END_REF]. These results revealed the presence of convergent validity in the measurement model. Uni-dimensionality was also confirmed by the AVE values (>0.50).

The variance explained by each principal factor was also tested to identify any potential common method bias [START_REF] Podsakoff | Self-reports in organizational research: Problems and prospects[END_REF]. Harman's one-factor test showed that the first factor only accounts for 23.542% of the total variance, which indicates that the common method bias would not be a serious problem. Furthermore, the correlation matrix (Table 5) shows that the highest inter-construct correlation is 0.602, while the common method bias is usually evidenced by extremely high correlations (r > 0.90) [START_REF] Bagozzi | Assessing construct validity in organizational research[END_REF]. Therefore, it is possible to state that the common method bias in this research was not a serious issue.

----

Table 4. Descriptive and psychometric table of measurements

Table 5 shows the discriminant validity of our variables measured with Likert scales. The square root of AVE was compared for each construct with correlations between each construct and the remaining constructs [START_REF] Fornell | Evaluating structural equation models with unobservable variables and measurement error[END_REF]. Each construct shared more variance with its own measurement items than with the constructs of the various measurement items. Therefore, discriminant validity was supported.

---

Descriptive statistics

Table 6 contains the descriptive statistics of the other variables included in the models. Customer satisfaction was the highest firm performance, looking at the mean values of the different firm performances. Financial performance was the second most appreciated outcome, while market performance was the least appreciated aspect. Considering the size of the companies, since we excluded small companies from the very beginning, companies had at least 50 employees and as high as 1,270 employees.

----Table 6. Descriptive statistics

Regression results

We used STATA 14 to conduct the regression analyses. In order to ensure that the multicollinearity effects were not an issue, the Variance Inflation Factor (VIF) was computed for each of the variables by running separate analyses in which one variable was the dependent variable while all the other variables were considered as independent. The VIF values ranged from 5.50 to 5.66. None of the VIF values reached the maximum acceptable level of 10. Thus, multicollinearity did not appear to be an issue.

Table 7 shows the regression results of our 12 models by using 2SLS regression approach with IV. Specifically, there are four sets of regression models. Model 1, Model 2 and Model 3 show that BDA business value leads to an increase of customer satisfaction, market performance and financial performance. Indeed, our hypotheses are on the environmental contingencies.

In our first set of Hypotheses, H1, we formulated that the higher the level of environmental dynamism, the higher the contribution of BDA solutions will be to the three performances of a firm: (H1a) financial performance, (H1b) market performance, and (H1c) customer satisfaction. Results of Model 4, Model 5 and Model 6 show that the interaction effect between business value and environmental dynamism is not significant for any dependent variable. This means that the dynamism of the environment does not have any effect in explaining the impact of business value on firm performance. For this reason, the first set of our Hypotheses H1 were all not supported.

In the second set of Hypotheses H2, we formulated that the higher the level of environmental munificence, the higher the contribution of BDA solutions will be to the three performances of a firm: (H2a) financial performance, (H2b) market performance, and (H2c) customer satisfaction.

Results of Model 7, Model 8 and Model 9 show that the interaction effect between BDA business value and environmental munificence was significant for all the three dependent variables. This means that the munificence of the environment strengthens the impact of BDA business value on the three firm performances. For this reason, the second set of Hypotheses H2, H2a, H2b and H2c, were supported.

We additionally made endogeneity tests in order to verify whether the chosen instruments are good or not. First we tested the null hypothesis H 0 that the IV are exogenous. Both Durbin (score) statistic and Wu-Hausman statistic in all the models have a statistically significant p-value, which allows to reject the null hypothesis that the IV are exogenous, thus supporting that they are endogenous. Second, we also tested for overidentification restrictions, where the null hypothesis is that the instrument set is valid and the model is correctly specified. The Sargan (score) Chi-squared has a pvalue above the significance threshold and therefore the test is not significant. This means that the instruments set was valid.

Discussions

In this study, we measured the extent of the translation of the BDA business value into firm performance and our results enrich the previous empirical studies on the subject [START_REF] Akter | How to improve firm performance using big data analytics capability and business strategy alignment?[END_REF][START_REF] Ren | Modelling quality dynamics, business value and firm performance in a big data analytics environment[END_REF]Wamba et al., 2017a[START_REF] Wamba | How 'big data'can make big impact: Findings from a systematic review and a longitudinal case study[END_REF]. Our main theoretical contribution concerns the enrichment of the literature about the integration of the inward looking strategic perspectives, around the RBV, and the outward looking strategic perspectives, around contingency theory. We show the benefits of combining RBV and contingency theories to explain the contribution of BDA solutions to firm performance. The RBV distinguishes business value from firm performance and helps to explain why BDA solutions contribute to the creation of a competitive edge (Wamba et al., 2017a(Wamba et al., , 2017b)). When, inspired by the RBV perspective, we consider the BDA solutions as a firm's resource, we can conclude that the big data is a resource satisfying the necessary conditions for the creation of a competitive advantage: big data can be rare, inimitable and non-substitutable.

Complementary, the contingency theory justifies the moderating role of environmental munificence and dynamism (Dale [START_REF] Stoel | IT capabilities and firm performance: A contingency analysis of the role of industry and IT capability type[END_REF], even if we discovered that only environmental munificent plays a significant role on the impact of big data. Industry-related environmental effects have regularly been recognized as possible important factors playing a moderating role on firm's performances, when looking at the impacts of IT [START_REF] Li | Information technology and firm performance: Linking with environmental, strategic and managerial contexts[END_REF]. Hence, companies must pay attention to these external variables, in addition to their endogenous mechanisms, in their pursuit of competitive advantage [START_REF] Burns | The management of innovation[END_REF]Thompson et al., 1992). Given the absence of specific demonstrations of the moderating role of environmental contingencies on firm performance for BDA solutions, we explicitly formulated two distinct set of hypotheses on the moderating role of two environmental contingencies for BDA solutions. To the best of the authors' knowledge, this is the first study to evaluate the importance, in the big data domain, of the moderating effects of environmental munificence and environmental dynamism. On one hand, companies in industries characterized by a munificent market profit the most from their BDA solutions as their solutions further the firms' performances. The business value extracted from these IT solutions will enhance the competitive advantage of the firm in cases of high munificence. Hence, firms in munificent industries have an additional incentive for investing in BDA solutions. BDA solutions could make companies better able to follow growing customer demands. Complementary, the innovation and the experimentation, promoted by BDA solutions, could be more easily converted to competitive advantage in highly munificent markets [START_REF] Tan | Harvesting big data to enhance supply chain innovation capabilities: An analytic infrastructure based on deduction graph[END_REF][START_REF] Wamba | How 'big data'can make big impact: Findings from a systematic review and a longitudinal case study[END_REF]. Moreover, the possible future orientation of BDA solutions could facilitate the generation of new forward looking strategic initiatives which best match a supporting and growing environment.

On the other hand, and against our expectations, environmental dynamism did not emerge as a significant moderator. We hypothesized that the higher the level of environmental dynamism, the higher the contribution of BDA solutions would have been to firm performance. Results show that the contribution is neither positive nor negative. Indeed, literature helps us explaining our results.

Even though several studies (Côrte-Real et al., 2017;Wang et al., 2016) leaded us to hypothesize a positive relationship, some other studies bring divergent views. Indeed, research has noted that IT may hinder a firm's capacity to adapt to radical changes in the environment, due to the rigidity of the fixed physical and technological artefacts of IT systems. Firms are often constrained by the limitations of rigid IT architectures, complex IS and disparate technologies to a point that the organisation is hindered from rapidly adapting to external changes [START_REF] Van Oosterhout | Change factors requiring agility and implications for IT[END_REF].

Beyond the direct technical issues, also the management of IT could lead to unintended firm rigidity in responding to radical environmental changes, like ignoring weak signals [START_REF] Lu | Understanding the link between information technology capability and organizational agility: An empirical examination[END_REF]. The role of IT in generating rigidity, rather than agility, in addressing the external contingencies could be particularly prominent in firms having invested in BDA facing dynamic contexts. This may be understood looking at the specificities of the BDA solutions. BDA solutions include high volumes of data, which could be time and cost consuming to accumulate to a satisfactory level. An unexpected change in the environment could make the accumulated volume of data less relevant, making the time and the cost required to accumulate them unrecoverable. Also the capabilities of an organization on big data could potentially be ineffective in highly dynamic environments [START_REF] Schilke | On the contingent value of dynamic capabilities for competitive advantage: The nonlinear moderating effect of environmental dynamism[END_REF]. When the environment changes, firms reactivate organizational responses that proved successful under similar situations in the past. This implies that unfamiliar states in the external environment are ignored or are treated in a similar manner to some other types of events encountered and understood in the past. When an apparently proven response to an identified problem exists in the organizational memory, experimentation with alternatives becomes less attractive. Overall, different forces oppose, one another, in defining the direction of the moderating influence of environmental dynamism and maybe, at the end, these forces balance, one another, making the influence of environmental dynamism not significant.

In synthesis, BDA solutions fit better in munificent industries, while the performance of BDA solutions is not influenced by turbulent environments. Hence, our integration of the two perspectives of the RBV theory and the contingency theory facilitates the comprehension of the possible fit between BDA, as a firm's resource, and the external contingency of a munificent environment, which accentuates the benefits of the firms' BDA resource. We contribute, hence, to the open debate about the role of industry characteristics and the value of IT resources, showing evidences of the advantages in the combination of the contingency theory with the RBV theory of the firm to explain the performances coming from BDA solutions.

Additionally, we posited that the large possible diversity in the applicability of big data needed a broad definition of firm performance. Hence, we chose to measure firm performance with three different dimensions: financial performance, market performance and customer satisfaction. Our results point out that big data maintains the promise of creating a three-fold firm performance. First, BDA solutions facilitate the entry of a firm onto new markets, the release of innovative products and the possibility of beating competitors. Second, BDA solutions help a company to satisfy its customers with better products and services than the competition. Third, BDA solutions enhance the financial performance of a firm, as far as customer retention, sales growth and profitability are concerned. Nonetheless, managers should be aware of the fact that some differences could emerge, depending on the particular IT artefacts in which they want to invest [START_REF] George | Big data and management[END_REF][START_REF] Lynch | Big data: How do your data grow?[END_REF][START_REF] Mayer-Schönberger | Big data: A revolution that will transform how we live, work, and think[END_REF][START_REF] Orlikowski | The Algorithm and the crowd: Considering the materiality of service innovation[END_REF][START_REF] Watson | Tutorial: Big data analytics: Concepts, technologies, and applications[END_REF].

Managerial implications

Previous research highlighted the need of managers to better understand whether, when, and how to innovate with BDA solutions [START_REF] Abbasi | Big Data Research in Information Systems: Toward an Inclusive Research Agenda[END_REF][START_REF] Agarwal | Big data, data science, and analytics: The opportunity and challenge for IS research[END_REF]Côrte-Real et al., 2017;[START_REF] Erevelles | Big Data consumer analytics and the transformation of marketing[END_REF][START_REF] Lavalle | Big data, analytics and the path from insights to value[END_REF][START_REF] Xu | Effects of big data analytics and traditional marketing analytics on new product success: A knowledge fusion perspective[END_REF]. Several risks have been identified in BDA investments [START_REF] Akter | How to improve firm performance using big data analytics capability and business strategy alignment?[END_REF]. The model we proposed provides those managers interested in big data implications [START_REF] Davenport | Big data at work: dispelling the myths, uncovering the opportunities[END_REF]IDC, 2016) a tool to understand the impact of BDA solutions on firm performance, integrating endogenous resources with exogenous conditions. For practitioners this study demonstrates how best to leverage the BDA solutions to achieve, to maintain competitive advantages and it provides support to justify BDA investments. The results indicate that BDA solutions enhance the firm performance according to the environmental features.

Our results point out that BDA maintains the promise of creating added value to companies and that this value creation is amplified in munificent industries. Hence, this enhancement is stronger when the market can support sustained growth in demand and have an increasing customer base.

Firms that have not yet decided to adopt BDA technologies can gain a perception of the advantages in terms of firm performances that are possible by adopting and effectively using BDA. Moreover, firms should acquire and develop their BDA solutions in relation to their contingencies. Firms should take into consideration their industry environment to find the satisfactory fit between their BDA investments and the industry characteristics. Our results indicate that managers working in munificent industries are the best placed to invest and profit from BDA solutions.

Complementary, software vendors of BDA can also exploit the results of this study to better segment the market. As far as munificent environments are contingencies that boost the contribution of BDA solutions to firm performances, software vendors should target, first, the munificent industry actors, in their BDA marketing campaigns. On the opposite, the firms in turbulent environments should not be a priority for software vendors, as vendors would have less arguments to convince prospects to adopt BDA solutions.

Limitations and directions for future research

This study has some limitations that open up interesting opportunities for future research. First, the study had a cross-sectional research design, in which all the measurement items were collected at the same point of time. A longitudinal study could extend this research by capturing the dynamics of the business value of BDA solutions on firm performances. Second, the research employed one data collection method for each portion of data. Multiple sources of data could be used to further verify the proposed research model. On one hand, data about the business value of BDA solutions and firm performance could be obtained from objective sources. On the other hand, data about environmental dynamism and environmental munificence could be gathered via the questionnaire.

Conclusions

Our study contributes to the understanding of the factors affecting the relationship between BDA solutions and firm performance. We particularly enrich the scientific knowledge around the integration of the RBV theory with the contingency theory, at the crossroads of technology and management sciences. Empirically, we demonstrated the extent to which BDA solutions can provide companies with a competitive advantage and the role played by environmental contingencies. On one hand, we highlighted the moderating and positive influence of environmental munificence on the relationship between BDA business value and firm performance. On the other hand, we stated the absence of a moderating influence of the environmental dynamism on the relationship between BDA business value and firm performance. The study offers evidence that the BDA business value brings higher firm performance where markets are in a growing phase.

Figure

  Figure 1. Research framework



  Real-time (data is updated in the database as the event occurs, with little or no latency),  Near-time (data is updated in the database at set and regular time intervals),  After a long time (data is updated in the database only once, or irregularly) The following question was included for Variety: Up to now, what are the sources of data of your company, beyond traditional databases? Respondents had a multiple choice possibility, among the following list of sources: Radio Frequency Identification system data  Clickstream data  Smart/intelligent/connectedmeters data or other smart/intelligent/connected object data  Global Positioning System data  Point Of Sales data or other transactional data sources  Social media posts  Weblogs posts  Microblogs (eg. Tweets) posts  Online portal content  Email message content  Other natural language text sources  Audio sources  Image sources  Video sources  Other sources_____Please specify The following question was included for Volume: Up to now, what is the total amount of data stored in all the database of your company? Respondents had a single choice possibility, among the following alternatives:

Figure 2 .

 2 Figure 2. The independent variable, BDA business value, as second order construct and the dependent variables as three separate constructs: financial performance, market performance and customer satisfaction

Table 2 . Operationalization of the independent and the dependent variables based on the Likert scale, collected via the questionnaire.

 2

Table 3 . Operationalization of the moderating and control variables collected via the Bureau Van Dijk DIANE database

 3

  The fourth subset, Model 10 to Model 12, refers to the moderating effect of environmental munificence and dynamism on the relationship between the BDA business value and every firm performance investigated. They show the regression results, including both interaction effects in every model. They take the following forms:

	Environmental munificence + b 47 BDA business value * Environmental
	dynamism + b 48 X t + ɛ t
	Model 11: Market performance = a 11 + b 49 BDA business value +b 50 Environmental dynamism + b 51
	Environmental munificence + b 52 BDA business value * Environmental
	munificence + b 53 BDA business value * Environmental dynamism + b 54 X t
	+ ɛ t
	Model 12: Customer satisfaction = a 12 + b 55 BDA business value +b 56 Environmental dynamism +
	b 57 Environmental munificence + b 58 BDA business value * Environmental
	munificence + b 59 BDA business value * Environmental dynamism + b 60 X t
	+ ɛ t
	Model 7: Financial performance = a 7 + b 28 BDA business value +b 29 Environmental dynamism +
	b 30 Environmental munificence + b 31 BDA business value *
	Environmental munificence + b 32 X t + ɛ t
	Model 8: Market performance = a 8 + b 33 BDA business value +b 34 Environmental dynamism + b 35
	Environmental munificence + b 36 BDA business value * Environmental
	munificence + b 37 X t + ɛ t
	Model 9: Customer satisfaction = a 9 + b 38 BDA business value +b 39 Environmental dynamism + b 40
	Environmental munificence + b 41 BDA business value * Environmental
	munificence + b 42 X t + ɛ t
	Model 10: Financial performance = a 10 + b 43 BDA business value +b 44 Environmental dynamism +
	b 45 Environmental munificence + b 46 BDA business value *

Table 5 . Correlation matrix of the measured scales for discriminant validity evaluation and square roots of the AVE as diagonal elements

 5

Table 7 . Regression results

 7