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From value to vision: Reimagining the possible with data analytics

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... In time, along with the advances in the big data era, the idea of leaning on data gave way to data-driven culture (DDC). The most referred definition in the literature comes from 2013 and considers DDC as "a pattern of behaviour and practices by a group of people who share a belief that having, understanding, and using certain kinds of data and information plays a crucial role in the success of the organizations" [25]. At that time, the data-driven culture was at the beginning of its development in organizations. ...
... Based on the definition of [25], we consider DDC as the collective attitudes, behaviors, and practices that create an organizational culture where data are regarded as a valuable asset. Data-driven culture is reflected through the managers' attitudes and behaviors toward utilizing information derived from data. ...
... According to Cosic et al. [46], the data-driven environment supports the managers' decision-making and problem-solving endeavours. Kiron et al. [25] argued that in a data-oriented culture, the decision-making process advances to a new level, enabling managers to perceive insights that were previously unseen. Szukits and Móricz [23] explored the effects of this type of culture (in their view, an analytical culture) and discovered that it strongly influences decision-making. ...
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Business intelligence and analytics (BI&A) have recently emerged as a strategic approach to managerial tasks, providing opportunities to improve work performance. Despite the growing interest in evaluating cases of BI&A adoption, to the best of our knowledge, few studies have addressed the influence of data-driven culture and the effects of BI&A adoption specifically on the work performance of managers. The aim of this study is to assess whether a data-driven culture predicts the adoption of BI&A in companies and its impact on decision-making effectiveness and managerial performance. This novel research model was tested with 180 managers from Romanian companies that work with BI&A tools. Based on PLS-SEM data analysis, our findings suggest that a data-oriented culture is a strong predictor of BI&A adoption and decision-making effectiveness. The results also confirm that BI&A utilization positively impacts decision-making effectiveness and individual work performance. The primary implication drawn from empirical evidence is that executives should prioritize the cultivation of a data-driven culture within their organizations, as this is essential for enhancing managerial performance through the adoption of business intelligence and analytics.
... Big data analytics capabilities. Big data analytics capabilities refer to the abilities to leverage data management, technology, and personnel resources to obtain business insights and boost competitiveness to realize full strategic potentials, and big data analytics capabilities thus consist of big data analytics management capabilities, big data analytics technology capabilities, and big data analytics personnel capabilities (Akter et al., 2016;Kiron et al., 2013;Lavalle et al., 2011;Wang et al., 2023). Among them, big data analytics management capabilities include the planning, coordination, investment, and control of big data analytics (Kiron et al., 2013). ...
... Big data analytics capabilities refer to the abilities to leverage data management, technology, and personnel resources to obtain business insights and boost competitiveness to realize full strategic potentials, and big data analytics capabilities thus consist of big data analytics management capabilities, big data analytics technology capabilities, and big data analytics personnel capabilities (Akter et al., 2016;Kiron et al., 2013;Lavalle et al., 2011;Wang et al., 2023). Among them, big data analytics management capabilities include the planning, coordination, investment, and control of big data analytics (Kiron et al., 2013). Big data analytics technology capabilities are the information systems that collect, store, process, and analyze big data (Rialti et al., 2019), and big data analytics personnel capabilities include management, technical, business, and relationship capabilities (Wamba et al., 2017). ...
... Big data analytics management capabilities's planning, co-ordination and control functions can be used to analyse disparate data to discover useful information and use it to improve knowledge exploitation. These functions can also be used to define big data analytics models used by the enterprise and build a cross-functional synchronization of the entire company analysis activities (Kiron et al., 2013). Big data analytics technology capabilities provide companies with various types of knowledge exploitation tools to improve coordination up and down the supply chain and to flexibly and quickly convert and exploit new organizational knowledge (Chen et al., 2017). ...
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While many organizations have successfully leveraged big data analytics capabilities to improve their performance, our understanding is limited on whether and how big data analytics capabilities affect social innovation in organizations. Based on the organizational information processing theory and the organizational learning theory, this study aims to investigate how big data analytics capabilities support social innovation, and how knowledge ambidexterity mediates this relationship. A total of 354 high-tech companies in China, this study shows that big data analytics management, big data analytics technology, and big data analytics personnel capabilities all have positive effects on social innovation. In addition, both knowledge exploration and knowledge exploitation play a mediating role in this process. Furthermore, a polynomial regression and response surface analysis shows that social innovation increases when knowledge exploration and knowledge exploitation are highly consistent but declines when knowledge exploration and knowledge exploitation are inconsistent. This study not only provides new perspectives for understanding how big data analytics capabilities contribute to social innovation, complementing the existing literature on big data analytics capabilities and social innovation, but also provides important practical guidance on how organizations can develop big data analytics capabilities to improve social innovation and solve social problems in the digital age.
... Data-driven culture has been around for decades and is sometimes interchanged with terms such as fact-based or data-oriented culture (Davenport et al., 2001). A data-driven culture is defined as "a pattern of behaviors and practices by a group of people who share a belief that having, understanding and using certain kinds of data and information plays a critical role in the success of their organization" (Kiron et al., 2013). Organizations are increasingly turning to data-driven decision-making practices. ...
... There is a consensus among scholars and industry leaders that merely acquiring, and scanning data is not enough to extract the best potentials . Firms therefore need a workforce with common pattern of behaviours, norms, and understandings on the essentials of data and information usage for competitive advantage (Kiron et al., 2013). A major challenge firms face in developing a datadriven culture is the lack of skills among their workforces (Davenport & Bean, 2018;Storm & Borgman, 2020). ...
... Although data-driven culture is expected to enhance organisational data usage and market scanning capabilities, acquisition of data and scanning it would not be enough to realise their full potentials Kiron et al., 2013). To extract the best performance, data-oriented organisations need to establish a corporate culture that includes, but not limited to, effective leadership and communication skills (Sjödin et al., 2018) as well as choice the right technology such as BAC (Holsapple et al., 2014;Kiron et al., 2012;Wang & Krisch, 2019). ...
... Additionally, studies elaborated that big data analytics (BDA) that consists of BDA decision-making, information integration, big data visualization techniques, and dynamic business environment is recognized as a pillar in an outlook of implementation of circular economy approaches in sense of CEP through HR practices (Gupta et al., 2019;Jabbour et al., 2019aJabbour et al., , 2019b. Big data analytics is further linked with data-driven culture (DDC) that consists of behaviors and practices as well as uses this information for organizational success in the framework of CEP (Holsapple et al., 2014;Kiron et al., 2013). ...
... Similarly, Kristoffersen et al. (2019) elaborated that data-driven culture accelerates circular economy performance. The studies highlighted that DDC is the pattern that consists of behaviors and practices and used this information for organizational success in the circumstance of CEP (Holsapple et al., 2014;Kiron et al., 2013). Tseng et al. (2018) established significant association among data-driven industrial culture and CEP, which is the key mechanism of industrialized symbiosis practices. ...
... It means that data-driven culture partially shows the impacts on the CEP. Likewise, the prior studies highlighted that data-driven culture is a design that consists of attitudes and activities and uses this information for organizational success in the mode of CEP (Holsapple et al., 2014;Kiron et al., 2013). The reason behind this insignificant moderator task of a data-driven culture among GI and CEP is that in different economies like Pakistan, data-driven culture is a new phenomenon that is less popular and lacks its applications, effectiveness, and implementation in the background of circular economy performance. ...
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The study illustrates the impact of green HR management on circular economy performance along with the mediator role of green innovation and moderator roles of big data analytics and data-driven culture. The 438 survey questionnaires were collected from textile sector SMEs and evaluated through PLS-SEM functionality. The study outcomes deliberated that green HRM has shown a significant positive impact on circular economy performance. Similarly, green innovation and big data analytics sanctioned mediators and moderator roles by focusing on circular economy performance. Therefore, data-driven culture did not perform as a moderator task between green innovation and circular economy performance. The study developed a hypothetical distinctive connection of resource base view theorem and absorptive capacity theory that recognized a firm’s resources or capabilities as new value, externally generated knowledge, and its implementation to accomplish the competitive benefit in an outline of circular economy performance. The SMEs will acquire advantage from this study in the perspective of new business systems, changing consumption patterns, re-cycling, repair, re-use, re-manufacturing, product sharing, and modularization for sustainable performance. The study would be exceedingly valuable for the foundation of policy documents regarding developing an environmental strategic tool kit in the outline of a green HR environment, big data involvement, and enhancement of circular economy performance with sustainable environmental protection.
... This diversity raises the question of whether the term "data-driven culture" carries a universal meaning across various researchers. The multifaceted nature of data-driven culture becomes apparent in the multitude of conceptual definitions spread across different research disciplines (Anderson, 2015;Gupta & George, 2016;Kiron et al., 2013;Medeiros & Maçada, 2022). ...
... In contrast, a digital culture emphasizes digital value objects like apps, platforms, and digital practices (Grover et al., 2022). On the other hand, a data-driven culture places its focus on information, which serves as a foundation for informed decision-making (Kiron et al., 2013). This emphasis on utilizing information can lead to additional value-adding mechanisms that ultimately impact business performance Oesterreich, Anton, Teuteberg, & Dwivedi, 2022). ...
... Such perspectives, however, allow for only one causal trajectory, occasionally incorporating individual-specific characteristics which may not suffice to manifest a robust data-driven culture. For example, Kiron et al. (2013) posits that the mere presence, comprehension, and utilization of data is adequate to develop a data-driven culture. In contrast, our definition integrates the dual facets of values and tools, and further enriches them with a configurational principle underpinning their manifestation. ...
Article
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Background: The role of a data-driven culture in improving organizational performance is widely recognized, but its conceptual definition lacks uniformity, leading to the existence of various constructs. This paper proposes a guiding framework for a data-driven culture, aiming to foster a unified understanding that aids both researchers and practitioners in the information systems (IS) field. Method: Adopting a qualitative research approach, this study conducts a systematic literature review to discern the breadth and depth of data-driven culture as portrayed in previous works. Alongside this, ten interviews were carried out with professionals well-versed in the application of data-driven strategies. Results: The study uncovers the multifaceted nature of a data-driven culture, highlighting its influence on decision-making practices within organizations. It identifies a range of characteristics relevant to the construct and consolidates these into an integrative framework, thereby developing a conceptual definition for data-driven culture. Conclusion: The paper contributes to the IS field by providing a framework that illuminates the concept of data-driven culture. This new understanding aids researchers in consistently theorizing the same phenomenon, supports the development of refined metrics for assessing data-driven culture, and paves the way for future research in this area. For practitioners, this framework delineates the characteristics of a data-driven culture and their interplay, enabling a more informed approach to cultural change efforts. Moreover, it highlights the importance of acknowledging the wider cultural context, and provides mechanisms to balance the emphasis on tools and values.
... Additionally, several studies have elaborated that big data analytics (BDA), which comprises BDA decision-making, information integration, big data visualisation techniques and dynamic business environment, is recognised as a pillar from the perspective of circular economic strategy adoption in CEP through human resource (HR) practices (Gupta et al. 2019;Jabbour et al. 2019a). Besides, BDA is further linked with the data-driven culture (DDC), comprising behaviours and practices, and this information is used for organisational success in the context of CEP (Holsapple, Lee-Post, and Pakath 2014;Kiron, Ferguson, and Prentice 2013). ...
... Similarly, Kristoffersen et al. (2019) elaborated that DDC accelerates CEP. These studies highlighted that DDC is a pattern of behaviours and practices, and this information is used for organisational success in the event of CEP (Holsapple, Lee-Post, and Pakath 2014;Kiron, Ferguson, and Prentice 2013). Tseng et al. (2018) established a significant association between data-driven culture and CEP; the critical mechanism of industrialised symbiosis practices. ...
... Hence, DDC partially shows an impact on CEP. Likewise, prior studies highlighted that DDC is a pattern comprising behaviours and practices and this information is used for organisational success in the CEP mode (Holsapple, Lee-Post, and Pakath 2014;Kiron, Ferguson, and Prentice 2013). The reason behind this trivial moderator task of DDC among green HRM and CEP is that in different economies, such as Pakistan, DDC is a new phenomenon, is less widespread, and needs wider application, greater effectiveness and implementation in the context of CEP. ...
Article
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This study aimed to illustrate the impact of green human resources management on circular economy performance; the mediator role of green innovation; and the moderator roles of big data analytics and data-driven culture. A total of 438 survey questionnaires were collected from small and medium-sized enterprises from the Pakistani textile sector and evaluated through partial least square-structural equation modelling. The study’s outcomes revealed that green human resources management significantly impacts circular economy performance. Similarly, green innovation and big data analytics mediated and moderated between green human resources management and circular economy performance. Nevertheless, data-driven culture is needed to moderate between green human resources management and circular economy performance. The study presented a unique and novel idea of green human resources management concerning circular economy performance – the best solution for sustainable environmental fortification in the current decade while enhancing circular economy performance.
... Although hospitals continue to invest heavily in digital transformation in the hope of achieving smart healthcare operations, they are struggling to gain the full benefits derived from practical applications on the ground, in real clinical contexts [4], [5]. The latest big data literature, on the other hand, also suggests that DDC is a critical organizational asset to gain potential benefits of big data and develop BDAC [12], [21], [22]. DDC is defined as "the extent to which organizational members (including top-level executives, middle managers, and lower-level employees) make decisions based on the insights extracted from data" [12, p. 1053]. ...
... In this article, DDC in the healthcare industry is defined as the extent to which healthcare delivery providers (e.g., managers and middle-level executives, physicians, nurses, and hospital staff) make data-driven business decisions based on actionable insights extracted from data, rather than on their professional instincts and past experiences [9], [12]. Developing DDC allows healthcare providers to make data-driven managerial and clinical decisions based on the insights derived from BDA, thereby using analytical insights to guide strategy, making timely and reliable treatment decisions, and predicting future patient needs [4], [5], [22]. ...
... Previous research has demonstrated that many big data projects fail because of the lack of data-driven decision-making culture rather than the lack of data or technology investment [22], [27], [40]. Organizational culture has been considered as one of the critical intangible resources that can facilitate the successful application of innovative information technology (e.g., [41], [42]). ...
Article
In the big data era, managing data-driven hospital operations have become one of the most important tasks for healthcare executives, increasing responsiveness to exceptional disruptions such as those caused by the COVID-19 pandemic. However, they are still facing the challenges of how best to orchestrate the digital medical resources for improving operational performance such as cost, delivery, and quality. Therefore, drawing upon resource orchestration theory, this article investigates how hospitals orchestrate data-driven culture (DDC) and digital technology orientation (DTO) to develop big data analytics capability (BDAC) for operational performance improvement. Survey data were collected from 105 hospitals in China and analyzed using structural equation modeling and ordinary least square regression. The results show that DDC has a significant positive impact on DTO. More interestingly, there is no significant interaction effect between DDC and DTO, indicating that DDC and DTO affect BDAC independently, and not synergistically. The results further reveal that BDAC fully mediates the DTO–operational performance relationship. The findings offer useful and timely guidance on how healthcare executives can manage data-driven hospital operations to improve operational performance during and post the COVID-19 pandemic.
... Thus, DDC addresses a collective pattern of shared behaviors and practices, which are based on the belief that having, understanding and using certain types of data and information plays a critical role in the organizational success (Kiron et al., 2013). In essence, the construct captures the importance that companies attach to data, and the extent to which they base their decisions on perception, rather than instinct (Mikalef et al., 2018). ...
... The four elements of business analytics develop a DDC where management decisions rely more on data-driven (Brynjolfsson and McElheran, 2016;Duan et al., 2020;Vidgen et al., 2017). The cultivation of a DDC is fundamental for the use of resources and analytical behavior in the interaction and interpretation of data, as well as in the effective use of BA in organizations (Kiron et al., 2013;Tabesh et al., 2019). Therefore, it is based on the following hypotheses that the DDC is an antecedent of analytical capabilities and CA. ...
... Therefore, it is plausible to consider that these elements are antecedents of BA. It would not be possible to apply advanced techniques, methods and processes without executive support and cultural aspects (Kiron et al., 2013); in addition, it would be challenging to succeed in using BA without the visualization features of identification, integration, immediacy and user interactivity with the MD data (Tay et al., 2018), capable of facilitating the analytical behavior and accelerating the visual reading for the analysis of BD (Park et al., 2016;Saggi and Jain, 2018;Sivarajah et al., 2017). Furthermore, evidence suggests that the critical point for obtaining value from the data is the generation of quick insights, which can be leveraged through BA (Seddon et al., 2017), which justifies the central role of analytics and OA in this model. ...
Article
Purpose In the digital age, the use of data and analytical capabilities to guide business decisions and operations plays a strategic role for organizations to gain competitive advantage (CA). However, the paths by which analytical capabilities convey their effect to CA are not yet fully known and few studies address the role of behavioral and cultural aspects of related of analytical capabilities. The purpose of this paper is to analyze how data-driven culture (DDC) and business analytics (BA) affect CA, considering the mediating effects of big data visualization (BDV) and organizational agility (OA). Design/methodology/approach A survey was conducted with 173 managers who are BDV and BA users in Brazilian organizations of various economic segments. The data were analyzed through structural equation modeling and mediation tests. Findings The evidence indicates that DDC and BDV are antecedents of BA. The following complementary mediations were discovered: BDV in the relationship between DDC and BA; BA in the relationship between DDC and CA; and OA in the relationship between BA and CA. It was also discovered that OA explains the transmission of most of the effect of BA to CA. Practical implications This study can help organizations to understand the importance of cultural and behavioral aspects related to the use of the analytical capabilities. Thereby, managers can establish policies and strategies to extract value from data and leverage business agility and competitiveness through use BDV and BA. Originality/value This study fills an important research gap by developing an original research model and discussing empirical evidence on how DDC and BA affect CA, considering the mediating effects of BDV and OA.
... Big data analytics (BDA) commonly describes the enhanced analytical techniques that process large data sets to derive useful knowledge (Abbasi et al., 2016;Chen et al., 2012;Davenport et al., 2010;LaValle et al., 2011). Effective use of BDA applications can help firms decipher hidden patterns in data providing business insights previously unimaginable (Kiron et al., 2013;Pauleen et al., 2017). For example, Amazon capitalizes on BDA insights from its proprietary consumer data to generate more than a third of its sales from personalized product recommendations (Wamba et al., 2017). ...
... For example, Oberweis-Dairy company uses public datasets based on community-level demographic information for its market segmentation and customer targeting. Its major success in expansion came in 2012, when it developed custom analytics reporting application that helped it discover several new segments of the customer market, contrary to conventional wisdom (Kiron et al., 2013). While such firms gain advantage, other firms can imitate them over time by using the same public datasets along with similar applications. ...
... These firms can evolve their BDA capabilities by leveraging their transformation capacity to customize their BDA solutions. For instance, LinkedIn, using job data similar to other public job portals, developed a custom in-house analytics algorithm helping it to achieve much online user higher click-rate compared to the (Kiron et al., 2013). LinkedIn leveraged its transformation capacity to develop an innovative custom solution and gained advantage. ...
Article
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Purpose While advances in big data analytics (BDA) provide valuable business insights and immense business value, many firms find it difficult to gain advantage from their BDA initiatives. Noting the strategic role of firm-specific knowledge, we develop a framework examining the relation between firm specificity of BDA knowledge and competitive advantage. We also examine the dynamic evolution of BDA capabilities and the associated knowledge management strategies. Design/methodology/approach We review the resource-based view (RBV), capabilities life cycles and absorptive capacity perspectives along with the literature on BDA competitive advantage. Identifying two key BDA factors, application customization and data proprietorship, we develop a BDA competitive advantage framework. We also investigate the absorptive capacities employed by firms to advance their BDA capabilities. We use anecdotal cases to support our theoretical arguments. Findings We propose that BDA solutions with vendor-based applications (noncustomized) and public data will not generate firm-specific knowledge and therefore not provide competitive advantage. In contrast, BDA solutions with custom applications and proprietary data will provide high-level firm-specific knowledge and potentially result in sustained competitive advantage. We further suggest the relevant absorptive capacities and the knowledge management strategies for BDA capability development. Practical implications Our framework provides managers with insights into how to develop and enhance firm-specific knowledge from their BDA solutions to gain competitive advantage. Originality/value Our study offers a new BDA firm-specific knowledge framework for competitive advantage.
... While it is suggested that in a retail bank context CRM decisions are often extensively relied on managerial heuristics (Persson & Ryals, 2014), there is indication that business analytics could be used to provide information for identifying and targeting prospective customers thereby developing and cultivating customer relationships (e.g. Kiron et al., 2013;LaValle et al., 2011). ...
... In this sense, market analytics use can be understood to demonstrate the firm's market-sensing capability that is the firm's ability to identify deep market insights and to sense market changes and unmet needs (Day, 1994). While evidence in the literature indicates that some firms use business/marketing analytics to enhance their CRM and brand management (Kiron et al., 2013), many firms in the retail industry do not perceive the potential benefit to be gained from deploying customer analytics. This may be because most firms are unsure how to proceed, thus it has been suggested that firms need to transform their capabilities so advanced analytics can become a core element in their efforts to improve performance. ...
... For example, research suggests that firms using business/marketing analytics to gain externally generated knowledge are more likely to be able to identify and target prospective customers thereby developing and cultivating customer relationships (e.g. Ashrafi & Zare Ravasan, 2018;Kiron et al., 2013;LaValle et al., 2011), to transform new product (Johnson et al., 2017), to have new product success (Xu et al., 2016), to differentiate its products, or to offer customer involvement so as to provide valuable input for developing new products. Additionally, Oztekin (2018) suggested that a firm gaining higher levels of marketing knowledge from data analytics will be more likely to maximize the benefits of CRM to build relationships with potential customers and to leverage the established relationship with customers thereby acquiring new customers and retaining customers (Vorhies & Morgan, 2005;Vorhies et al., 2011). ...
Article
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Purpose Evidence in the literature has indicated that customer-linking marketing capabilities such as customer relationship management (CRM) and brand management are important drivers of marketing performance and that marketing analytics use (MAU) enables firms to gain valuable knowledge and insights for improving firm performance. However, there has been little focus on how firms improve their CRM and brand management via MAU. This study aims to draw on the absorptive capacity theory, research on marketing capabilities and marketing analytics to examine the capability-developing mechanisms that enable a firm to use marketing analytics to enhance its CRM and brand management capabilities, thereby improving its marketing performance. Design/methodology/approach A research model is developed and tested based on an analysis of 289 responses collected using an online survey from middle and senior managers of Chinese firms with sufficient knowledge and experience in using marketing analytics for survey participation. Findings The findings demonstrate that MAU is positively related to both CRM and brand management capabilities, which in turn are positively associated with marketing performance; and that both CRM and brand management capabilities mediate the relationship between MAU and marketing performance. Research limitations/implications The study’s outcomes were based on data collected from a survey, which was distributed using mass e-mails. Thus, the study is unable to provide a meaningful response rate. The research results are based on and limited to Chinese firms. Practical implications MAU is essential for enhancing customer-linking marketing capabilities such as CRM and brand management, but it alone is not sufficient to improve marketing performance. Firms wishing to improve marketing performance should leverage the knowledge and insights gained from MAU to enhance their critical customer-linking marketing capabilities. Originality/value This study explicates the capability-developing mechanisms through which a firm can use its market-sensing capability as manifested by MAU to enhance customer-linking marketing capabilities and to improve its marketing performance. In so doing, this study extends our understanding of the critical role of absorptive capacity in helping firms identify, assimilate, transform and apply valuable external knowledge.
... The model theory is rooted in the RBV , Hazen et al., 2012, Zhao et al.,2010and Grant, 1991 and relational sociomaterialism (Kim, Shin, Kim, & Lee, 2011;Kim, Shin, & Kwon, 2012;Orlikowski, 2007;Orlikowski & Scott, 2008). The BDAC model is also based on PODCs, and the emerging literature on BDA (Davenport, Barth, & Bean, 2012;Kiron, Ferguson, & Prentice, 2013). The model investigates the effect of BDAC on FP. ...
... BDAC is broadly defined as the competence to provide business insights using data management, infrastructure (technology), and talent (personal) capabilities to transform the business into a competitive force (Kiron et al., 2013). Similarly, constructs such as BDA infrastructure capability, big data management capability, and PODCs are adapted from Kim et al. (2011), Kim et al., 2012. ...
... Adapted from Kiron et al. (2013), Kim et al. (2012, p. 335, 336), and Wamba et al. (2017) Organizational culture (CL) CL is described as the beliefs and values shared within the organization, which help in forming the patterns of behaviour of employees. KN is the capacity of organizational members to use their personal experiences, values, beliefs, and discretion to analyse their organizational environment and enhance performance. ...
Article
Firms are increasingly relying on business insights obtained by deploying data analytics. Analytics-driven business decisions have thus taken a strategic imperative role for the competitive advantage of a firm to endure. The extent and effectiveness through which business firms can actually derive benefits by deploying big data-based practices requires deep analysis and calls for extensive research. This study extends the big data analytics capability (BDAC) model by examining the mediatory effects of organizational culture (CL) between internal analytical knowledge (KN) and BDAC, as well as the mediating effects of BDAC between CL and firm performance. The findings bring into focus that CL plays the role of complementary mediation between BDAC and KN to positively impact firm performance (FP); BDAC also plays a similar mediatory role between CL and the performance of a firm.
... Effective DDDM requires the clear and compelling communication of data-driven insights to decision-makers and other stakeholders. Data visualization tools, such as charts, graphs, and dashboards, can be used to present complex data in a more accessible and intuitive format, facilitating the interpretation and understanding of data-driven insights (Kiron et al., 2013). ...
... Despite the potential benefits of data-driven decision-making, it is crucial to recognize that the successful implementation of DDDM depends on several factors, such as the quality and relevance of the data, the appropriateness of the analytical techniques used, and the organization's ability to integrate data-driven insights into its broader decision-making processes. Additionally, organizations must address potential ethical and privacy concerns related to the collection, storage, and use of data, ensuring that they comply with relevant regulations and industry standards (Kiron et al., 2013). ...
Article
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This study explores the impacts of participatory design (PD) on data-driven decision-making (DDDM) in organisations. Despite the extensive examination of PD and DDDM individually, there is a dearth of research in understanding their integration and their impact on decision-making processes in organisations. This research aims to fill this gap by investigating the potential impacts, challenges, benefits, and critical success factors associated with the incorporation of PD activities into DDDM. The study employs a systematic literature review methodology to provide a comprehensive understanding of the topic. The paper provides a research agenda for future researchers as well as discussing best practices for organizations seeking to optimise their data driven decision-making processes in a participatory manner. The research also discussed the ethical implications of data-driven decision-making. Ultimately, this research advances our understanding of how PD and DDDM can be effectively combined to achieve better decision-making outcomes.
... Three out of the 22 articles with definitions came from the management literature (Kiron et al. 2013;Kiron and Shockley 2011;Kiron et al. 2012). ...
... Most of the 22 articles either quoted or slightly rephrased the definitions of (Kiron et al. 2013;Kiron and Shockley 2011;Kiron et al. 2012) or (Gupta and George 2016). However, seven articles provided unique definitions of data-driven culture, see Table 2. ...
Conference Paper
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Establishing a data-driven culture in teams is on the agenda for many managers and analytics leaders. With a data-driven culture in place, it is envisioned that investments in analytics can be used to their full potential. In practice, most organizations struggle to establish a data-driven culture in teams and have few tools available to assess the level of maturity. Related research has focused on maturity models in business intelligence & analytics that target the organizational level. Hence, these maturity models provide limited support for assessing the team level, e.g., why some teams do not develop a data-driven culture. This paper used a systematic literature review and an online questionnaire to develop a matrix for assessing a team's maturity in data-driven culture. The matrix synthesizes previous work in analytics and group development. Findings from the literature review revealed a mismatch between problems addressed by the research community and perceived problems in practice by organizations.
... The data analytic process captured in Fig. 3 describes an information value chain: data is captured, aggregated, integrated, and analyzed with the promise of gaining information that can guide future action (Kiron et al., 2013). As we have described in our discussion of analytic readiness factors, this process can be stymied by issues upstream at collection (e.g., poor quality or coverage of metrics), aggregation (e.g., harmonization) and storage access (e.g., centralized databases) phases, as well as downstream where the data is analyzed and disseminated (Kiron et al., 2013). ...
... The data analytic process captured in Fig. 3 describes an information value chain: data is captured, aggregated, integrated, and analyzed with the promise of gaining information that can guide future action (Kiron et al., 2013). As we have described in our discussion of analytic readiness factors, this process can be stymied by issues upstream at collection (e.g., poor quality or coverage of metrics), aggregation (e.g., harmonization) and storage access (e.g., centralized databases) phases, as well as downstream where the data is analyzed and disseminated (Kiron et al., 2013). Our framework for assessing these key phases of the analytic process thus offers an additional method of evaluation for the ultimate goal-value creation. ...
Article
Big data and analytics have shown promise in predicting safety incidents and identifying preventative measures directed towards specific risk variables. However, the safety industry is lagging in big data utilization due to various obstacles, which may include lack of data readiness (e.g., disparate databases, missing data, low validity) and personnel competencies. This paper provides a primer on the application of big data to safety. We then describe a safety analytics readiness assessment framework that highlights system requirements and the challenges that safety professionals may encounter in meeting these requirements. The proposed framework suggests that safety analytics readiness depends on (a) the quality of the data available, (b) organizational norms around data collection, scaling, and nomenclature, (c) foundational infrastructure, including technological platforms and skills required for data collection, storage, and analysis of health and safety metrics, and (d) measurement culture, or the emergent social patterns between employees, data acquisition, and analytic processes. A safety-analytics readiness assessment can assist organizations with understanding current capabilities so measurement systems can be matured to accommodate more advanced analytics for the ultimate purpose of improving decisions that mitigate injury and incidents.
... In the last years, the increasing amount of data that companies have been called to process and their potential key role in making strategic decisions has attracted the attention of managers and scholars (De Mauro et al. 2018;Erevelles et al. 2016;Gnizy 2018;Lopez-Nicolas and Soto-Acosta 2010;Sivarajah et al. 2017). The conventional practices, based on the exploitation of structured, small, and centralized data, have been recently challenged by the development of innovative information systems able to simultaneously process different semi-structured and unstructured datasets (Bean and Kiron 2013;Kiron et al. 2013;Germann et al. 2014;Grover et al. 2018;Vera-Baquero et al. 2016). The process of extracting, generating, interpreting, and categorizing useful information through the compression of an enormous amount of data is nowadays also known as big data analytics (BDA) (Chen et al. 2013;. ...
... The exploitation of big data can in fact have positive effects in several domains, such as customer relationship management, operational risk management, and overall firm operational efficiency and performance (Bresciani et al. 2018;Germann et al. 2014;Kiron et al. 2013;Mikalef et al. 2019b;Wamba et al. 2017a, b). The infinite amount of detailed information made available by BDA allows managers to be increasingly informed on the states of different business processes, such as supply chain, workforce performances, internal operations, and behavioral patterns of consumers (Bresciani et al. 2018;Dubey et al. 2018). ...
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In order to face the challenges of internationalization and to cope more efficiently with the uncertainty of foreign expansion, firms are called to analyze an increasing amount of real-time semi-structured and unstructured datasets. In this sense, big data analytics (BDA) can become strategic in stimulating the international growth of small and medium-sized enterprises (SMEs). However, the specific relationship between BDA and internationalization has been analyzed fragmentarily within the mainstream literature. With the purpose of shedding light on this relationship, the authors drew on resource-based view (RBV) and collected data through a questionnaire directed to CEOs of 266 SMEs, receiving 103 responses. A quantitative analysis based on an Ordinary Least Squares (OLS) regression showed that the relationship between governance of BDA infrastructure and the degree of internationalization (DOI) is not significant, while the direct effect of BDA capabilities as well as the interaction term between BDA infrastructure and BDA capabilities are positive and significant. This suggests that the governance of BDA per se is not enough for enhancing internationalization in SMEs. On the contrary, this article points out the relevance of developing specific BDA capabilities and the existence of a positive interplay between governance of BDA infrastructure and BDA capabilities that can exploit the new knowledge coming from BDA in SME international growth.
... A data-driven culture is manifested in employees' attitudes toward, and beliefs and opinions about, data-driven decision-making (Arunachalam et al., 2018). This type of culture is generally considered present when there is a shared understanding that the use of data is critical to firm success (Kiron et al., 2013). Organizational learning refers to the prevalence of conditions that enable employees to acquire, assimilate, and effectively use BA-related competencies and knowledge (Fink et al., 2017;Jeble et al., 2018;Stein and Vandenbosch, 1996). ...
... There have been numerous research and reviews published recently on big data analytics, implementations, and related technologies as a result. [12]. ...
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The rapid pace of technological advancement necessitates constant adaptation. As a rapidly evolving field, project management has little choice but to take use of technological breakthroughs to stay relevant and fresh. Using big data analytics, businesses and project managers alike can reap the benefits of this technology. Big data analytics is definitely useful for influencing the future of project management, as outlined in this article's preliminary comments. A new era of 21st-century living appears to be upon us, and project management as a profession appears to be ready to embrace it. Big data collects and stores enormous amounts of data that are becoming increasingly difficult to manage and analyze. The potential benefits and competitive edge of this new technology are motivating the majority of businesses to invest in big data analytics. Structured or unstructured, large amounts of heterogeneous data are processed and managed as "Big Data" in the enterprise. This includes both structured and unstructured data. Analytic methods and technologies are heavily employed in the management and analysis of large and complex data sets for use in a wide range of applications that enhance the performance of a business. This paper analyzes the impact of big data and business analysis on project management. A literature review is followed by primary and secondary data analysis, which includes interviews and surveys for architectural analysis, in an exploratory study. Qualitative and quantitative data are the norm in all studies in this study. The present study is descriptive in nature, as the goal is to examine the impact of big data and business analysis on project management. Below mentioned impacts have been analyzed and concluded that big data analytics helps to reduce the project complexities, reduces the project cost and enhances the project risk management. ____________________ * Email: raed.zitar@sorbonne.ae. † School of Medicine.
... A few recent studies have described BDA as a strategic component used for managing customer relations, operational risks, and overall operations of firms to maximize their financial performance (Bresciani et al. 2018;Germann et al. 2014;Kiron et al. 2013;Mikalef et al. 2019;. From a managerial perspective, BDA offers infinite data to streamline business processes, supply chains, and workforce performance, as well as to improve organizations' internal collaboration and analyze consumers' behavioral patterns (Bresciani et al. 2018;Dubey et al. 2019). ...
Article
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Today’s dynamic business environment has pushed service-oriented firms such as banks to collaborate with external partners through open innovation (OI) to address issues of service differentiation, optimize customer experience, and create effective open innovation strategies (OIS). However, the essential elements required to design OIS and the methods to manage these strategies are missing. Therefore, this study aims to investigate the strategic resources essential to creating OIS and identify the tools to manage these resources. Following the fundamentals of the resource-based view (RBV), bank openness (BOP), selection of external partners (SEP), open innovation methods (OIM), formalizing collaboration processes (FCP), and banks’ internal practices (BIP) are identified as the strategic elements required for creating OIS, and the role of big data analytics (BDA) in these strategic resources is examined. The data were collected through a survey questionnaire from 425 bank executives employed at different digital banks located in Malaysia. To achieve our research objectives, a quantitative deductive research design was employed and the collected data were processed in WarPLS using the structural equation modeling (SEM) technique to test the research hypotheses of this study. The empirical results reveal that BDA has a significant positive impact on BOP, SEP, and FCP, whereas OIM and BIP have an insignificant positive impact. The findings of this study contribute to designing a robust digital strategy to enhance the banking sector’s contribution to the development of financial industries in developing countries by employing BDA as a major strategic policy tool of OIS
... In the early 21st century, big data analytics has been identi ed as one of the key drivers for maintaining Kiron, 2013). With the advent of commodity hardware (to process big data), computer processing power (thanks to Moore's Law), the maturity of computer and software engineering, network bandwidth, increasingly low cost of data storage, companies are able to capture, process, transform and analyze a large volume of data. ...
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Big data analytics has emerged as an important area of research in the big data domain. However, big data analytics capability empirical investigations have been hindered by the lack of psychometrically sound measurement items and scales. This paper reports approaches to the development and validation of new multi-item measurement scales reflecting a construct called data analytics capability. Data analytics capability reflects an organization's expertise in developing and deploying resources, usually in combination, to achieve the desired new insights that have business implications. This data analytics capability competence is operationalized as a multidimensional construct reflected by measurement items. In the first stage of measure development, we review the prominent information systems journals. We develop the construct items most of which are new items. Then we use industry experts and academicians to score them on a one to five scale. We also asked them to propose any new items. Based on their evaluation and scoring we finalize four items for this construct. Our results demonstrate that a reduced set of measurement items have reasonable psychometric properties and, therefore, are useful inputs for multi-item measurement scale development. In the second stage of measurement development, we conduct a survey of big data users via two online user groups. We received 349 valid responses which are analyzed using SPSS and AMOS statistical software. We successfully performed construct reliability analysis. The construct developed in this research may be used to advance scholarly understanding and theory in the big data and data analytics field.
... Mas para atingir efetivamente esse aproveitamento é necessária uma mudança na cultura de tomada de decisão (McAfee et al., 2012;Abbasi et al. 2016;Wedel e Kannan 2016;Capelas, 2019). A cultura baseada em dados é referida como "um padrão de comportamento e práticas por um grupo de pessoas que compartilham a crença de que ter, compreender e usar certos tipos de dados e informações desempenha um papel crucial no sucesso de suas organizações" (Kiron, Ferguson, & Prentice, 2013). ...
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Objetivo: Instituições de Ensino Superior (IES) possuem uma variedade de dados com potencial de gerar informações que podem contribuir com a tomada de decisões e uma melhor administração dos recursos. Nesse contexto, este artigo visa discutir o tema Academic Analytics em relação aos seus conceitos, estágios e desafios. Desenho: foi empregado o gênero de ensaio teórico para o desenvolvimento de reflexões a partir da abordagem sociotécnica, considerando a experiencia deste autor, bem como estudos teóricos e empíricos da literatura. Resultados: a partir da articulação de diferentes conceitos, uma definição integrativa sobre Academic Analytics é apresentada; bem como os estágios e desafios gerenciais no desenvolvimento da inteligência analıtica em IES, incluindo dimensões como liderança, governança de dados, cultura e pessoas, política, ética e tecnologia. Contribuições acadêmicas e práticas: a idiossincrasia do contexto, a lente da abordagem sociotécnica, e a perspectiva focada em gestão permite um olhar sistêmico e prático sobre o tema. Espera-se ainda que gestores e lideranças institucionais de IES, bem como pesquisadores, possam tomar as reflexões como um ponto de partida para projetos no âmbito organizacional e acadêmico que envolvam o desenvolvimento de suas competências analíticas.
... Business analytics can render organizations more data-driven (Kiron et al., 2013), and in this context, they need to analyze huge volumes of customer data sets. This requires acknowledging the importance of information technology and data science (Sharma et al., 2014). ...
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In the present digital environment, a data-driven organizational culture has become a vital emerging driver of organizational growth. This data-driven culture has assumed an advanced shape due to adoption of artificial intelligence (AI) integrated business analytics tools in the organization. Data-driven culture in the organization could considerably impact product innovation strategy as well as organizational process alteration. In this context, the aim of this study is to investigate how an organization’s data-driven culture impacts process performance and product innovation that led to enhanced organizational overall performance and higher business value. Methodologically, supported by relevant extant literature and inputs from the resource-based view and dynamic capability theories (organizational context), a conceptual model and a set of hypotheses are initially developed. These are subsequently statistically validated through a survey involving 513 usable responses from employees of different organizations using business analytics tools embedded with AI capability. The findings demonstrate that an organizational data-driven culture has considerable moderating impact on product innovation and process improvement, which ultimately enhance business value through improved organizational overall performance.
... The work highlights that the use of HR analysis in strategic decision making depends on the organizational culture and leadership that promotes data-driven decision making and data-driven culture. This culture, according to Kiron, Ferguson, and Prentice (2013), concerns the existence of a pattern of behaviors and practices of a group of people who share the belief that understanding and using data and information plays a critical role in the success of their organization. ...
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Introdução: este estudo objetiva desenvolver uma revisão sobre o tema People Analytics baseada na literatura da área disponível nas bases de dados Web of Science e Scopus. O conceito foi explorado, assim como estudos que demonstram como utilizá-lo nas organizações, as vantagens e desafios de sua aplicação e a percepção de gestores sobre esta aplicação. Método: a pesquisa foi realizada a partir de artigos disponíveis nas bases de dados acima citadas, publicados a partir do ano de 2005. Resultados: os resultados mostram que especial atenção deve ser dada a origem dos dados analisados, assim como uma análise correta do material disponível é fundamental para que possa gerar informação e conhecimento que representem valor para as organizações. Conclusão: destaca-se ainda que a capacitação dos profissionais de Recursos Humanos para o uso de dados é enfatizada em diversos artigos que compuseram a análise deste estudo.
... But merely acquiring data through BA and scanning the environment would not fully serve the purpose, because the firm must exhibit its reaction to the everchanging external marketing needs to achieve peak success. This has been endorsed by dynamic capability view (DCV) (Helfat Davenport and Harris (2007) The study discussed extensive use of data, statistical tools, quantitative analysis, as well as explanatory and predictive models for the fact-based management decision-making process in organizations Kiron et al. (2013) The paper discussed data-driven culture. It was referred to as a pattern of behavior and practices by a group of people. ...
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Data-driven culture is considered to bring business-oriented cultural transformation to a firm. It is considered to provide substantial dividends to the firms’ product and process innovations. Recently, several firms have been using different advanced technology-embedded business analytics (BA) tools to improve their business performance. Again, advancement of information and communication technology has helped firms to explore the option to use BA tools with artificial intelligence. This has brought radical change in the business-oriented cultural landscape of the firms to arrive at accurate decision-making to improve their innovation and performance. In this perspective, the aim of this study is to show how a firm’s data-driven culture impacts its product and process innovation, which in turn improves its performance and provides better competitive advantage in the current business environment. With the help of background study, a resource-based view model and different theories, a conceptual model has been developed. The conceptual model has been validated with 456 usable responses from the employees of different firms using different business analytics tools. The study highlights that data-driven culture highly influences both product and process innovation, making the firm more competitive in the industry. In this study, leadership support and data-driven culture have been taken as moderators, whereas firm size, firm age and industry type have been taken as control variables.
... In the context of data analytics, this touches on several aspects that are equally relevant in private-sector companies, but are often exacerbated in public-sector organizations. For instance, the existence of both organizational and data silos is one of the most critical challenges to overcome when leveraging data analytics ( Kiron, Ferguson & Kirk Prentice, 2013 ). As Desouza and Jacob (2017) outline, this fragmentation is commonplace in public-sector organizations, further amplifying the associated challenges. ...
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In this work, we investigate the challenges public-sector organizations face when seeking to leverage prescriptive analytics and provide insights into the public value such data-driven tools and methods can provide. Using the strategic triangle of value, legitimacy, and operational capacity as a starting point, we derive a framework to assess public-sector prescriptive analytics initiatives, along with six guiding questions that structure the assessment process. We present a case study applying prescriptive analytics to the placement of charge points in urban areas, a critical challenge many municipalities are currently facing in the transition towards electric mobility. Reflecting on the analytics application as well as its development and implementation process through the guiding questions, we derive key lessons for public-sector organizations seeking to apply prescriptive analytics.
... For example, Oakland Athletics adopted sabermetrics in 2002 and accomplished higher performances relative to its low budget for scouting free agent players for approximately two years. However, as other big market teams began to apply sabermetrics to its strategy, the data-driven decision-making did not continue to work as a core competency for the Oakland Athletics (Kiron, Ferguson, & Prentice, 2013). Third, the longitudinal nature of MLB data allows us to test our theoretically grounded hypotheses of intangible resource inimitability; ...
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We propose that due to the decreased spectrum of available strategies (i.e., social simplicity) and simplified mechanisms of value creation (i.e., causal clarity) associated with a greater reliance on data-driven decisions in highly competitive and specialized industries, the positive effects of social capital for data analytics on firm performance will diminish when firms predominantly adopt data-driven decision-making in deploying human resources. Alternatively, the positive effect associated with social capital in data analytics is more profound when moderated by intuition-based (i.e., idiosyncratic knowledge) decision-making for managing HR selections. To test our hypotheses, we observed 30 major league baseball (MLB) teams over 6 years from 2009 to 2014 as the early phase of the ‘big data era’ that began as a result of PITCHf/x tracking systems in all MLB stadiums being implemented. Our findings suggest that the introduction of the system to all ballparks and resulting dispersion of specialized human capital across this industry facilitates firm specificity of data analytic knowledge to become generic overtime.
... The effect of missing data, and inaccurate and meaningless information may negatively affect companies. Quite often organizations struggle with the accuracy of data underpinning day-to-day decisions [37]. It is important to address the data in motion challenges, given that data observations might be lost in decision-making algorithms. ...
Conference Paper
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Data generated by sensors in the Internet of Things (IoT) space experiences data loss. This data loss can be caused by many occurrences including network failure, faulty sensors, duplicates, unreliable sensors, and other factors. This paper attempts to make decisions knowing that there is data loss. We used neural network as a promising approach taking into consideration the nature of data we used. Data used in this paper is fast growing, labelled, and can be lost due to afore mentioned possibilities. Hence, we adopted a supervised learning approach that works better with labelled data. We first theoretically evaluated convolutional neural network (CNN), K-nearest neighbor (KNN), naïve Bayes, support vector machine (SVM), logistic regression and long-short term memory (LSTM) as promising and potential classifiers/algorithms for a waste collection use case. There has been a challenge of finding an effective waste collection method. The best performing classification model showed high accuracy, recall, precision, and f1 score when working out lost data and is able tell us what action to take. These two decisions are very important to save cost and/or to protect the environment (citizens) in metropolitan areas. The results indicate that the data loss threshold that can be taken per sample is 40% data loss. The results also indicate which algorithm to use at 10%, 20% and 30% of data loss.
... Business analytics is one of the areas business schools have embraced in their curriculum updates. This phenomenon is unsurprising given the attention business analytics and its relationship to big data has received in the popular press and across various industries (LaValle et al. 2011;Kiron et al. 2013;Richards 2017;Kent 2018a, b). Business programs often require a class covering data analytics in some form or another that focuses on quantitative analysis (Scala et al. 2018). ...
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Undergraduate business students, like many other undergraduate majors, are often apprehensive about the quantitative courses required to earn their degree. Active learning methods, including flipped classrooms, have been studied as approaches to mitigate these fears among students. With our overarching goal of helping students improve their understanding of quantitative business concepts, we implemented a novel active learning method called the Learning Assistant model. Using an experimental design holding the instructor and the student assessments constant, we report the results of the first-known implementation of this technique in a business course. As indicated by the change in the students’ final numerical grades, this pedagogical technique shows promise in helping students master the material better than those who took the course in a traditional lecture-based learning environment.
... The effect of missing data, and inaccurate and meaningless information may negatively affect companies. Quite often organizations struggle with the accuracy of data underpinning day-to-day decisions [16]. It is important to address the data in motion challenges, given that data observations might be lost in decision-making algorithms. ...
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Metropolitan cities often experience waste collection challenges due to ineffective methods of collection. This paper described criteria and an approach for efficient decision-making for waste collection that will make use of data generated by IoT-enabled objects. This implies taking into account multi-objective goals in the collection process while dealing with complexities such as data loss during IoT based data collection. Understanding current decision-making algorithms highlights the deeper insight required for IoT based decision-making algorithms. There is a need for decision-making algorithms to be dynamic so that they can address different levels of data loss inherent in IoT data collection. This paper presents the criteria to be considered and a model for smarter decisions in the smart city as applied to waste collection.
... It influences customer likes, dislikes behaviors and buying decisions [17]. Social media platforms become powerful marketing channel to increase customer awareness on business product and its service offerings [19]. SMA operations such as recommending users and communities [22], Influence users, and top trending topics identification are popular and it has been used in brand awareness, product or service development, and politics. ...
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The Social, Mobile, Analytics and Cloud (SMAC) explosion in recent times changed the way the customers look at and collaborate businesses through large world of data or information, often described as “Big Data”. With over 1,590 million active users, social media such as Facebook, Twitter, WhatsApp, Instagram, LinkedIn etc. send or receive messages or post or access new content every day. Businesses or enterprises understand and extract useful insights from social media platform and transforming it into useful information or knowledge along with their enterprise business data for strategic decision making. A framework for Social Media Analytics based on Multi-Criteria Decision Making (MCDM) model is proposed for social media data and our comprehensive study on large-scale twitter dataset experiment explains how MCDM (TOPSIS) method outperforms against the standard centrality methods
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Purpose Big data marketing analytics (BDMA) has been discovered to be a key contributing factor to developing necessary marketing capabilities. This research aims to investigate the impact of the technology and information quality of BDMA on the critical marketing capabilities by differentiating between firms with low and high perceived market performance. Design/methodology/approach The responses were collected from marketing professionals familiar with BDMA in North America ( N = 236). The analysis was done with partial least squares-structural equation modelling (PLS-SEM). Findings The results indicated positive and significant relationships between the information and technology quality as exogenous constructs and the endogenous constructs of the marketing capabilities of marketing planning, implementation and customer relationship management (CRM) with mainly moderate effect sizes. Differences in the path coefficients in the structural model were detected between firms with low and high perceived market performance. Originality/value This research indicates the critical role of technology and information quality in developing marketing capabilities. The study discovered heterogeneity in the sample population when using the low and high perceived market performance as the source of potential heterogeneity, the presence of which would likely cause a threat to the validity of the results in case heterogeneity is not considered. Thus, this research builds on previous research by considering this issue.
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Business analytics is considered in research and practice as a promising approach to support organizations in the increasing complexity and dynamics in the strategic planning and decision making, which arise for example through the integration of product-service systems. However, literature is lacking a comprehensive analysis to what extant business analytics supports the strategic planning and decision making. Thus, coming from the affordance and socio-technical system theory, we are linking business analytics affordances to strategic planning outcomes. In doing so, we identified 20 affordances which we have assigned to the dimensions of the socio-technology system theory. Based on this, we have derived implications and propositions for research and practice. The results can be used as guidelines for practice and directions for future research.
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This paper presents a systematic literature review of the research field on big data analytics capabilities (BDACs). With the emergence of big data and digital transformation, a growing number of researchers have highlighted the need for organizations to develop BDACs. Despite valuable efforts to examine determinants and contributions to performance measures, the research field on BDACs remains relatively unexplored. The review reveals a patchwork of studies lacking a theoretical and conceptual foundation and questions arise regarding the reliability and validity of predominantly survey-based empirical studies. Drawing on findings from related capability concepts, this paper suggests the use of clearer definitions and items and a greater variety of methods to facilitate further exploration of BDACs. Finally, future research areas and implications are outlined.
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Kültür, sahip olunan değerler bütünüdür. Farklı gruplar, örgütler ve toplumlardaki insanlar arasındaki temel değerler, tutumlar ve davranışlardaki farklılıkları ele alır. Artık şirketler, kişiler ve kurumlar veri olmadan bilgiyi yönetememektedirler. Bilgi yönetimi örgüt kültürünün gelişiminde önemli katkı sağlamaktadır. Örgüt kültürünün bir parçası haline gelen veri ve bilgi kullanımı ile birlikte bilgi paylaşım davranışı işletmelerin yüksek performanslı çalışmasını sağlayabilecektir. Bu çalışmada büyük veri ve örgüt kültürü kavramları açıklanmış, ayrıca örgütsel davranışın büyük veri ve örgüt kültürüne etkisi tartışılarak sonuç ve önerilere yer verilmiştir.
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Studies indicate that organizational capability is a key factor in operational performance, and that both sensing and analytics capabilities have a significant influence on operational performance. This study develops a framework to examine the impact of organizational capability on operational performance, with a specific focus on the implementation of sensing and analytics capabilities. We combine strategic fit theory, the dynamic capability view, and the resource-based view to examine how micro, small, and medium enterprises (MSMEs) strategically integrate a data-driven culture (DDC) with their organizational capabilities to enhance operational performance. We carry out empirical research to investigate whether a DDC moderates the influence of organizational capability on operational performance. Structural equation modeling of survey data from 149 MSMEs reveals that both sensing and analytics capabilities have a positive impact on operational performance. The results also suggest that a DDC positively moderates the influence of organizational capability on operational performance. We discuss the theoretical and managerial implications of our findings, the limitations of the study, and opportunities for further research.
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Globalized economies have organizations seeking ways to implement Business Analytics (BA) though ample organizations fail in such initiatives. Hence, scholars embark on IT competence enabled BA to inspire a data-driven culture. Such is a crucial prerequisite for fostering long term organizational sustainable competitiveness. Even though the above rationale receives widespread attention, there is scant evidence comprehending whether IT competence enabled BA to empower an organizational data-driven culture to achieve long-term sustained competitive advantage. Current literature reviews are conducted predominantly through journals and academic conference papers. The literature review reveals a conceptual model composed of three propositions viable for future research's empirical confirmation. This study defuses IT competence, business analytics, data-driven culture, and organizational competition in one model, proving through critiqued literature that business analytics inspires mined data visualizations that encourage a data-driven organizational culture when enabled by IT competence. Such a culture, in turn, helps an organization achieve the evidence-based managerial decisions which help organizations sustain a long-term competitive advantage. Such a model bares theoretical and practical implications also portrayed in the article. KeywordsIT competenceBusiness analyticsData-driven cultureDecision-makingSustainable competition
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In this study, the linguistic tones of managers and audience during the UK earnings conference calls have been extracted and examined the differences between them. By using a sample of non-financial UK firms listed in the FTSE 350 index over the period 2010–2015, the authors found that manager tones convey much more optimism (less pessimism) than the audience counterparts in the UK earnings conference calls. This result supports academics and practitioners in understanding the language communication during the UK earnings conference calls. Overall, this study is beneficial for regulators, policymakers, and professionals, in evaluating the narrative information disclosed during the UK earnings conference calls.
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Despite the increasing number of companies deploying big data analytics (BDA) in corporate social responsibility (CSR) activities, few studies have investigated how the use of BDA in CSR activities affects CSR performance. Drawing on the resource-based view, we propose that the impact of BDA use in CSR initiatives (BDA-enabled CSR) on CSR performance depends on a firm's ability to provide data-driven insights through big data management and big data analytics (big data analytics capability, BDAC). We further show that the positive interaction effect of BDA-enabled CSR and BDAC on CSR performance is pronounced in the CSR performance categories of environmental impact, employee relations, product safety, and corporate governance, but not in community relations, human rights, and workplace diversity. This study contributes to BDA literature as well as CSR literature by empirically demonstrating how BDA-enabled CSR and BDAC influence CSR performance. This study provides practical implications to strategy managers, social entrepreneurs, venture capitalists or investors, and policy makers.
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This chapter reviews the literature on the use of business analytics in higher education. Universities have large datasets available to predict future direction and generate actionable information. An important type of analytics used to improve management processes and to make informed decisions is big data business analytics. State university executive leaders may improve the effectiveness of their decisions by integrating business analytics in the decision-making models. However, there is a need to examine the use of big data business analytics in the decision-making process at the executive leadership level of the selected state universities. Especially in the context of how descriptive, predictive, prescriptive, decisive and basic analytics, and data collection influence the decision-making process at the executive leadership level of the state universities in terms of student retention and graduation rates.
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Objetivo: defender a tese de que estratégia, cultura e governança de dados são determinantes no modo como a organização obtém vantagem competitiva por meio da ciência de dados. Método: este ensaio está fundamentado em uma revisão teórica de estudos empíricos e conceituais para a identificação e definição de construtos e desenvolvimento de proposições, e de um modelo de conceitual. Originalidade/Relevância: na Era Digital, o Big Data e a Ciência de Dados redefiniram a produtividade, a inovação e a competitividade. Contudo, o sucesso no uso da Ciência de Dados depende do adequado alinhamento entre os fatores estratégicos. Resultados: considera-se que o modelo organizacional, formado pela estratégia, cultura e governança de dados, beneficia o uso da Ciência de Dados. Conclui-se, então que, para suportar a transformação digital, as organizações precisem formular sua estratégia de dados, além de estabelecer a composição ideal entre cultura e governança, a fim de direcionar suas capacidades analíticas e desbloquear o potencial da Ciência de Dados em prol da vantagem competitiva. Contribuições Teóricas: o modelo teórico proposto é original por combinar construtos relacionados à gestão estratégica da Ciência de Dados, estabelecendo as bases para a compreensão de suas inter-relações, e descrevendo a relação destes com a vantagem competitiva. Contribuições para a Gestão: o modelo teórico proposto pode ser utilizado tanto para direcionar a gestão estratégica dos dados, como para balancear o alinhamento estratégico organizacional que influencia no uso da Ciência de Dados, bem como para avaliar o sucesso das iniciativas analíticas e as vantagens competitivas obtidas.
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Conference Paper
Drawing upon the knowledge-based view and dynamic capability theory, we examine the effects of managerial and employee analytics human capital on firm performance in the context of big data analytics. We posit that both these types of human capital are sources of competitive advantage and superior firm performance; and that their impacts on firm performance are mediated through dynamic and productive capabilities. We test our hypotheses using the data from Fortune 500 firms. The results confirm that both managerial and employee analytics human capital, as well as their interaction, have a significant positive impact on firm performance. We also find modest support for the mediating effect of dynamic capability in the relationship between managerial analytics human capital and firm performance. Finally, we find strong support for the mediating effect of productive capability in the relationship between employee analytics human capital and firm performance. Overall, this empirical study helps to confirm the key role played by analytics human capital, and mediating capabilities in improving firm performance.
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The dawn of 4th industrial evolution (4.0) in emerging economies, has been a paradigm shift for data consumption in cement companies, where value input from marketing analytics is apparent but holistic understanding is lacking. The cement industry is facing problems in terms of utilising the benefit from marketing analytics reservoirs, at the organisational level, by taking initiatives, depicting issues and remedial steps, and projecting the future possibilities. The objective of this research was to fill this gap. For this purpose, a series of interview sessions from various levels of management at the cement company head office was conducted, followed by transcription, codification, funnelling and triangulation. The results depicted that 'initiatives & benefits' stage is most significant. The collective learning is about cross-functionality of the marketing department for competency designs (marketing and logistics), teaming with IT, training for insight reports understanding, the inclination to data-driven decisions, a steady but comparatively slow shift to analytics in overall marketing operations.
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Big Data has received great attention in academic literature and industry papers. Most of the experiments and studies focused on publishing results of big data technologies development, machine learning algorithms, and data analytics. To the best of our knowledge, there is not yet any comprehensive empirical study in the academic literature on big data technology acceptance. The statistical results of this model provide a compelling explanation of the relationships among the antecedent variables and the dependent variables. The analysis of the structural model reveals that the hypothesis tests are significant for 8 out of 12 path relationships.
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This chapter reviews the literature on the use of business analytics in higher education. Universities have large datasets available to predict future direction and generate actionable information. An important type of analytics used to improve management processes and to make informed decisions is big data business analytics. State university executive leaders may improve the effectiveness of their decisions by integrating business analytics in the decision-making models. However, there is a need to examine the use of big data business analytics in the decision-making process at the executive leadership level of the selected state universities. Especially in the context of how descriptive, predictive, prescriptive, decisive and basic analytics, and data collection influence the decision-making process at the executive leadership level of the state universities in terms of student retention and graduation rates.
Article
Sustainability has become a global corporate mandate with implementation impacted by two key trends. The first is recognition that global supply chains have a profound impact on sustainability which requires “greening” the entire supply chain. The second is technology—digitization, artificial intelligence (AI), and “big data”—which have become ubiquitous. These technologies are impacting every aspect of how companies organize and manage their supply chains and have a powerful impact on sustainability. In this essay, we synthesize current dominant themes in research on sustainable supply chains in the age of digitization. We also highlight potential new research opportunities and challenges and showcase the papers in our STF.
Article
Back in the 1990s, computer engineer and Wall Street "quant" were the hot occupations in business. Today data scientists are the hires firms are competing to make. As companies wrestle with unprecedented volumes and types of information, demand for these experts has raced well ahead of supply. Indeed, Greylock Partners, the VC firm that backed Facebook and LinkedIn, is so worried about the shortage of data scientists that it has a recruiting team dedicated to channeling them to the businesses in its portfolio. Data scientists are the key to realizing the opportunities presented by big data. They bring structure to it, find compelling patterns in it, and advise executives on the implications for products, processes, and decisions. They find the story buried in the data and communicate it. And they don't just deliver reports: They get at the questions at the heart of problems and devise creative approaches to them. One data scientist who was studying a fraud problem, for example, realized it was analogous to a type of DNA sequencing problem. Bringing those disparate worlds together, he crafted a solution that dramatically reduced fraud losses. In this article, Harvard Business School's Davenport and Greylock's Patil take a deep dive on what organizations need to know about data scientists: where to look for them, how to attract and develop them, and how to spot a great one.
Reinventing Society in the Wake of Big Data
  • A Pentland
A. Pentland, "Reinventing Society in the Wake of Big Data," August 30, 2012, www.edge.org.
The Digital Advantage: How Digital Leaders Outperform Their Peers in Every Industry
  • Capgemini Consulting
  • Center
  • Business
Capgemini Consulting and MIT Center for Digital Business, "The Digital Advantage: How Digital Leaders Outperform Their Peers in Every Industry," November 5, 2012, www.capgemini.com.
Moneyball Strikes Again: How to Use Analytics for Sustained Competitive Advantage
  • L Melnick
L. Melnick," Moneyball Strikes Again: How to Use Analytics for Sustained Competitive Advantage," October 3, 2012, http://lloydmelnick.com. 15. The two questions were: (a) To what extent does information and business
The two questions were: (a) To what extent does information and business analytics create a competitive advantage for your organization within its industry or markets? (b) To what extent do you agree with the following statement? Analytics has helped improve my organization's ability to innovate
  • L Melnick
L. Melnick," Moneyball Strikes Again: How to Use Analytics for Sustained Competitive Advantage," October 3, 2012, http://lloydmelnick.com. 15. The two questions were: (a) To what extent does information and business analytics create a competitive advantage for your organization within its industry or markets? (b) To what extent do you agree with the following statement? Analytics has helped improve my organization's ability to innovate.