scholarly journals The role of alliance management, big data analytics and information visibility on new-product development capability

2021 ◽  
Author(s):  
Rameshwar Dubey ◽  
David J. Bryde ◽  
Gary Graham ◽  
Cyril Foropon ◽  
Sushma Kumari ◽  
...  
Keyword(s):  
Big Data ◽  
Product Development ◽  
Competitive Advantage ◽  
Organizational Performance ◽  
New Product Development ◽  
Data Analytics ◽  
Dynamic Capabilities ◽  
Big Data Analytics ◽  
New Product ◽  
Alliance Management

AbstractMany organizations are increasingly investing in building dynamic capabilities to gain competitive advantage. New products play an important role in gaining competitive advantage and can significantly boost organizational performance. Although new product development (NPD) is widely recognized as a potentially vital source of competitive advantage, organizations face challenges in terms of developing the right antecedents or capabilities to influence NPD performance. Our research suggests that organizations should invest in building alliance management capability (AMC), big data analytics capability (BDAC) and information visibility (IV) to achieve their desired NPD success. Informed by the dynamic capabilities view of the firm (DCV) we have stated seven research hypotheses. We further tested our hypotheses using 219 usable respondents gathered using a pre-tested instrument. The hypotheses were tested using variance based structural equation modelling (PLS-SEM). The results of our study paint an interesting picture. Our study makes some significant contribution to the DCV and offers some useful directions to practitioners engaged in NPD in the big data analytics era. We demonstrate that AMC and BDAC are lower-order dynamic capabilities and that AMC has a positive and significant influence on BDAC. In turn, AMC and BDAC influence NPD under the moderating influence of IV. Ours is one of the first studies to empirically establish an association among three distinct dynamic capabilities which are often considered in isolation: AMC, BDAC and NPD. Our findings support emergent views on dynamic capabilities and their classification into various orders. Lastly, we provide empirical evidence that information visibility acts as a contingent variable to both AMC and BDAC effects on NPD. We end our paper by outlining some limitations of our study and by offering useful future research directions.

Business model designs, big data analytics capabilities and new product development performance: evidence from China

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Biao Sun ◽  
Yu Liu
Keyword(s):  
Big Data ◽  
Product Development ◽  
New Product Development ◽  
Business Model ◽  
Data Analytics ◽  
Big Data Analytics ◽  
New Product ◽  
Moderating Effects ◽  
Content Type ◽  
Npd Performance

PurposeAlthough the business model (BM) has become a top priority in management research, existing literature still offers a confusing and partial picture about how to leverage BM designs for new product development (NPD) because of two limitations. First, research has paid little attention to different BM designs' effects on NPD performance. Second, few empirical studies have examined the moderating roles of firms' learning capabilities, such as big data analytics capabilities (BDA capabilities). This study aims to investigate the effects of BM novelty design and BM efficiency design on NPD performance and the ways in which BDA capabilities moderate these effects.Design/methodology/approachA literature review provides the model and hypotheses. Using a sample of 208 Chinese firms, the authors conducted an empirical test following multiple regression analysis.FindingsThe results demonstrate that BM novelty design has a positive effect on NPD performance while BM efficiency design takes the form of an inverted U-shape. Moreover, BDA capabilities (i.e. BDA technology capability and BDA management capability) have complicated moderating effects on BM novelty design- and BM efficiency design-NPD performance relationships.Research limitations/implicationsThe results may be affected by both the context (solely in China) and type (cross-sectional) of the data set. This study has explored the moderating effects of BDA capabilities, further studies considering other significant practices such as social media usage, could yield richer insights that would help validate the results of this study.Practical implicationsFirst, we suggest that managers should be explicitly aware of the different impacts of BM novelty design and BM efficiency design on NPD performance. Second, this study encourages managers to build relevant BDA capabilities to work with BM designs to improve NPD performance.Originality/valueThis is one of the first studies to investigate BM designs' complicated influences on NPD success and explore BDA capabilities' moderating effects on the BM design-NPD performance linkage.

Application of Statistical Analysis Tools and Concepts to Big Data and Predictive Analytics to New Product Development

2020 ◽  
Vol 3 (1) ◽  
pp. 17-35
Author(s):  
Brian J. Galli
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Data Analysis ◽  
Product Development ◽  
Competitive Advantage ◽  
New Product Development ◽  
New Product ◽  
Big Data Analysis ◽  
Life Cycles ◽  
The Impact

In today's fiercely competitive environment, most companies face the pressure of shorter product life cycles. Therefore, if companies want to maintain a competitive advantage in the market, they need to keep innovating and developing new products. If not, then they will face difficulties in developing and expanding markets and may go out of business. New product development is the key content of enterprise research and development, and it is also one of the strategic cores for enterprise survival and development. The success of new product development plays a decisive role both in the development of the company and in maintaining a competitive advantage in the industry. Since the beginning of the 21st century, with the continuous innovation and development of Internet technology, the era of big data has arrived. In the era of big data, enterprises' decision-making for new product development no longer solely relies on the experience of decision-makers; it is based on the results of big data analysis for more accurate and effective decisions. In this thesis, the case analysis is mainly carried out with Company A as an example. Also, it mainly introduces the decision made by Company A in the actual operation of new product development, which is based on the results of big data analysis from decision-making to decision-making innovation. The choice of decision-making is described in detail. Through the introduction of the case, the impact of big data on the decision-making process for new product development was explored. In the era of big data, it provides a new theoretical approach to new product development decision-making.

scholarly journals Strategies to Implement Big Data Analytics in Telecommunication Organizations

2021 ◽  
Vol 10 (04) ◽  
pp. 064-076
Author(s):  
Dr. Delton Aneato ◽  
Dr. Cesar Castellanos
Keyword(s):  
Big Data ◽  
Competitive Advantage ◽  
Organizational Performance ◽  
Data Analytics ◽  
Big Data Analytics ◽  
The United States ◽  
Communication Strategy ◽  
Analysis Process ◽  
Successful Communication ◽  
Multiple Case

Information technology (IT) leaders who do not invest in big data projects may struggle to gain a competitive advantage and business insights to improve performance. Grounded in Kotter’s change and Six Sigma models, the purpose of this qualitative multiple case study was to explore strategies IT leaders use to implement big data analytics successfully. The participants comprised 4 IT leaders from 2 telecommunication organizations in the United States of America, who expertly used big data analytics strategies to promote and maximize competitive advantage. Data were collected from semistructured interviews, company documents, and project-related documents. The collected information was examined by utilizing a thematic analysis approach. Four themes emerged from the data analysis process communication, training, employee involvement in decisions, and teamwork strategy. A key recommendation from these findings is for IT leaders to use successful communication strategies to convey the vision and objectives to all organizational levels. The successful communication-strategy can help evaluate business trends, forecasts, improve overall organizational performance and competitive advantage. The implications for positive social change include the potential for job creation, thus catalysing economic growth within communities.

scholarly journals How Big Data Analytics Boosts Organizational Performance: The Mediating Role of the Sustainable Product Development

2020 ◽  
Vol 6 (4) ◽  
pp. 190
Author(s):  
Saqib Ali ◽  
Petra Poulova ◽  
Fakhra Yasmin ◽  
Muhammad Danish ◽  
Waheed Akhtar ◽  
...  
Keyword(s):  
Air Pollution ◽  
Big Data ◽  
Product Development ◽  
Organizational Performance ◽  
Data Analytics ◽  
Time Lag ◽  
Big Data Analytics ◽  
Haze Pollution ◽  
Sustainable Product Development ◽  
Sustainable Product

Increasing haze pollution and its adverse effects on human health is pressuring academics and practitioners to search for different solutions for environmental sustainability around the world. Similar to other countries, Pakistan is also affected by air pollution, and smog has become a fifth season. In Pakistan, one of the main reasons of smog and air pollution is hazardous emissions from vehicles. As a result, the booming automobile industry of Pakistan is now affected by two major challenges: sustainable product development and organizational performance. To meet these challenges, the study has developed a conceptual model to find the effect of big data analytics on organizational performance by adopting a sustainable development program. For the elimination of standard method biases, the study has used a time lag approach to collect the data in three waves and receive 372 usable responses. The empirical results of PLS-SEM suggest that big data analytics have a positive effect on a sustainable product development and sustainable product development has a positive and significant impact on organizational performance. Moreover, mediation of a sustainable program development is also confirmed between big data analytics and organizational performance. The managerial and theoretical implications of these results are discussed.

scholarly journals Traditional marketing analytics, big data analytics and big data system quality and the success of new product development

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ahmad Ibrahim Aljumah ◽  
Mohammed T. Nuseir ◽  
Md. Mahmudul Alam
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
New Product Development ◽  
Data Analytics ◽  
Big Data Analytics ◽  
New Product ◽  
Data System ◽  
System Quality ◽  
Content Type ◽  
Traditional Marketing

PurposeThis study investigates the impact of traditional marketing analytics and big data analytics on the success of a new product. Moreover, it assesses the mediating effects of the quality of big data system.Design/methodology/approachThis study is based on primary data that were collected through an online questionnaire survey from large manufacturing firms operating in UAE. Out of total distributed 421 samples, 327 samples were used for final data analysis. The survey was conducted from March–April 2020, and data analysis was done via Structural Equation Modelling (SEM-PLS).FindingsIt emerges that big data analysis (BDA), traditional marketing analysis (TMA) and big data system quality (BDSQ) are significant determinants of new product development (NPD) success. Meanwhile, the BDA and TMA significantly affect the BDSQ. Results of the mediating role of BDSQ in the relationship between the BDA and NPD, as well as TMA and NPD, are significant.Practical implicationsThere are significant policy implications for practitioners and researchers concerning the role of analytics, particularly big data analytics and big data system quality, when attempting to achieve success in developing new products.Originality/valueThis is an original study based on primary data from UAE.

scholarly journals Big data analytics capability in supply chain agility

2019 ◽  
Vol 57 (8) ◽  
pp. 2092-2112 ◽  
Author(s):  
Rameshwar Dubey ◽  
Angappa Gunasekaran ◽  
Stephen J. Childe
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Competitive Advantage ◽  
Data Analytics ◽  
Dynamic Capabilities ◽  
Big Data Analytics ◽  
Content Type ◽  
Moderation Effect ◽  
Supply Chain Agility ◽  
Analytics Capability

Purpose The purpose of this paper is to examine when and how organizations build big data analytics capability (BDAC) to improve supply chain agility (SCA) and gain competitive advantage. Design/methodology/approach The authors grounded the theoretical framework in two perspectives: the dynamic capabilities view and contingency theory. To test the research hypotheses, the authors gathered 173 usable responses using a pre-tested questionnaire. Findings The results suggest that BDAC has a positive and significant effect on SCA and competitive advantage. Further, the results support the hypothesis that organizational flexibility (OF) has a positive and significant moderation effect on the path joining BDAC and SCA. However, contrary to the belief, the authors found no support for the moderation effect of OF on the path joining BDAC and competitive advantage. Originality/value The study makes some useful contributions to the literature on BDAC, SCA, OF, and competitive advantage. Moreover, the results may further motivate future scholars to replicate the findings using longitudinal data.

scholarly journals Big data analytics and firm performance: Effects of dynamic capabilities

2017 ◽  
Vol 70 ◽  
pp. 356-365 ◽  
Author(s):  
Samuel Fosso Wamba ◽  
Angappa Gunasekaran ◽  
Shahriar Akter ◽  
Steven Ji-fan Ren ◽  
Rameshwar Dubey ◽  
...  
Keyword(s):  
Big Data ◽  
Firm Performance ◽  
Data Analytics ◽  
Dynamic Capabilities ◽  
Big Data Analytics ◽  
Performance Effects

Big data analytics capabilities and organizational performance: the mediating effect of dual innovations

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Xiaofeng Su ◽  
Weipeng Zeng ◽  
Manhua Zheng ◽  
Xiaoli Jiang ◽  
Wenhe Lin ◽  
...  
Keyword(s):  
Big Data ◽  
Organizational Performance ◽  
Empirical Analysis ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Mediating Effect ◽  
Content Type ◽  
Mediating Role ◽  
The Relationship

PurposeFollowing the rapid expansion of data volume, velocity and variety, techniques and technologies, big data analytics have achieved substantial development and a surge of companies make investments in big data. Academics and practitioners have been considering the mechanism through which big data analytics capabilities can transform into their improved organizational performance. This paper aims to examine how big data analytics capabilities influence organizational performance through the mediating role of dual innovations.Design/methodology/approachDrawing on the resource-based view and recent literature on big data analytics, this paper aims to examine the direct effects of big data analytics capabilities (BDAC) on organizational performance, as well as the mediating role of dual innovations on the relationship between (BDAC) and organizational performance. The study extends existing research by making a distinction of BDACs' effect on their outcomes and proposing that BDACs help organizations to generate insights that can help strengthen their dual innovations, which in turn have a positive impact on organizational performance. To test our proposed research model, this study conducts empirical analysis based on questionnaire-base survey data collected from 309 respondents working in Chinese manufacturing firms.FindingsThe results support the proposed hypotheses regarding the direct and indirect effect that BDACs have on organizational performance. Specifically, this paper finds that dual innovations positively mediate BDACs' effect on organizational performance.Originality/valueThe conclusions on the relationship between big data analytics capabilities and organizational performance in previous research are controversial due to lack of theoretical foundation and empirical testing. This study resolves the issue by provides empirical analysis, which makes the research conclusions more scientific and credible. In addition, previous literature mainly focused on BDACs' direct impact on organizational performance without making a distinction of BDAC's three dimensions. This study contributes to the literature by thoroughly introducing the notions of BDAC's three core constituents and fully analyzing their relationships with organizational performance. What's more, empirical research on the mechanism of big data analytics' influence on organizational performance is still at a rudimentary stage. The authors address this critical gap by exploring the mediation of dual innovations in the relationship through survey-based research. The research conclusions of this paper provide new perspective for understanding the impact of big data analytics capabilities on organizational performance, and enrich the theoretical research connotation of big data analysis capabilities and dual innovation behavior.

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