scholarly journals Operationalizing Business Model Innovation through Big Data Analytics for Sustainable Organizations

2019 ◽  
Vol 12 (1) ◽  
pp. 277 ◽  
Author(s):  
Vinicius Luiz Ferraz Minatogawa ◽  
Matheus Munhoz Vieira Franco ◽  
Izabela Simon Rampasso ◽  
Rosley Anholon ◽  
Ruy Quadros ◽  
...  
Keyword(s):  
Big Data ◽  
Performance Management ◽  
Business Model ◽  
Small Businesses ◽  
Data Analytics ◽  
Design Science ◽  
Big Data Analytics ◽  
Business Model Innovation ◽  
Raw Material ◽  
Animal Origin

Business model innovation is considered key for organizations to achieve sustainability. However, there are many problems involving the operationalization of business model innovation. We used a design science methodology to develop an artifact to assist business model innovation efforts. The artifact uses performance measurement indicators of the company’s business model, which are powered by Big Data analytics to endow customer-driven business model innovation. Then, we applied the artifact in a critical case study. The selected company is a fashion ecommerce that proposes a vegan and sustainable value using recycled plastic bottle yarn as raw material, and ensures that no material with animal origin is used. Our findings show that the artifact successfully assists a proactive and continuous effort towards business model innovation. Although based on technical concepts, the artifact is accessible to the context of small businesses, which helps to democratize the practices of business model innovation and Big Data analytics beyond large organizations. We contribute to the business model innovation literature by connecting it to performance management and Big Data and providing paths for its operationalization. Consequently, in practice, the proposed artifact can assist managers dealing with business model as a dynamic element towards a sustainable company.

Big data analytics for investigating Taiwan Line sticker social media marketing

2019 ◽  
Vol 32 (2) ◽  
pp. 589-606 ◽  
Author(s):  
Shu-Hsien Liao ◽  
Szu-Yu Hsu
Keyword(s):  
Social Media ◽  
Big Data ◽  
Business Model ◽  
Data Analytics ◽  
Business Models ◽  
Model Development ◽  
Big Data Analytics ◽  
Social Media Marketing ◽  
Content Type ◽  
Purchase Behaviors

Purpose Line sticker, a social media, it allows users to exchange multimedia files and engage in one-to-one and one-to-many communication with text, pictures, animation and sound. The purpose of this paper is to examine various Taiwan user experiences in the Line sticker use behaviors. Further, this research looks at how the situations of Line sticker proprietors and their affiliates are disseminated for formulating social media marketing (SMM) in its business model concerns. Design/methodology/approach This study examines the experience of various Taiwanese Line stickers users utilizing a market survey, a total of 1,164 valid questionnaire data, and the questionnaire is divided into five sections with 30 items in terms of the database design. All questions use nominal and order scales. This study develops a big data analytics approach, including cluster analysis and association rules, based on a big data structure and a relational database. Findings The authors divide Taiwan Line sticker users into three clusters by their profiles and then find each group’s social media utilization and online purchase behaviors for investigating the Line sticker SMM and business models. Originality/value This is the first study to offer a big data analytics to investigate and analyze the varieties in the use of Line sticker by exploring users’ behaviors for further SMM and business model development.

scholarly journals Design Science Research: Evaluation in the Lens of Big Data Analytics

Systems ◽  
2019 ◽  
Vol 7 (2) ◽  
pp. 27 ◽  
Author(s):  
Ahmed Elragal ◽  
Moutaz Haddara
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Design Research ◽  
Research Evaluation ◽  
Design Science ◽  
Big Data Analytics ◽  
Science Research ◽  
Design Science Research ◽  
Evaluation Phase ◽  
New Type

Given the different types of artifacts and their various evaluation methods, one of the main challenges faced by researchers in design science research (DSR) is choosing suitable and efficient methods during the artifact evaluation phase. With the emergence of big data analytics, data scientists conducting DSR are also challenged with identifying suitable evaluation mechanisms for their data products. Hence, this conceptual research paper is set out to address the following questions. Does big data analytics impact how evaluation in DSR is conducted? If so, does it lead to a new type of evaluation or a new genre of DSR? We conclude by arguing that big data analytics should influence how evaluation is conducted, but it does not lead to the creation of a new genre of design research.

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.

Big data analytics and demand forecasting in supply chains: a conceptual analysis

2018 ◽  
Vol 29 (2) ◽  
pp. 739-766 ◽  
Author(s):  
Erik Hofmann ◽  
Emanuel Rutschmann
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Conceptual Analysis ◽  
Scientific Literature ◽  
Design Science ◽  
Demand Forecasting ◽  
Big Data Analytics ◽  
Scientific Work ◽  
Content Type ◽  
Forecasting Methods

Purpose Demand forecasting is a challenging task that could benefit from additional relevant data and processes. The purpose of this paper is to examine how big data analytics (BDA) enhances forecasts’ accuracy. Design/methodology/approach A conceptual structure based on the design-science paradigm is applied to create categories for BDA. Existing approaches from the scientific literature are synthesized with industry knowledge through experience and intuition. Accordingly, a reference frame is developed using three steps: description of conceptual elements utilizing justificatory knowledge, specification of principles to explain the interplay between elements, and creation of a matching by conducting investigations within the retail industry. Findings The developed framework could serve as a guide for meaningful BDA initiatives in the supply chain. The paper illustrates that integration of different data sources in demand forecasting is feasible but requires data scientists to perform the job, an appropriate technological foundation, and technology investments. Originality/value So far, no scientific work has analyzed the relation of forecasting methods to BDA; previous works have described technologies, types of analytics, and forecasting methods separately. This paper, in contrast, combines insights and provides advice on how enterprises can employ BDA in their operational, tactical, or strategic demand plans.

scholarly journals Business Intelligence and Big Data Analytics for Organizational Performance Management in Public Sector: The Conceptual Framework

2016 ◽  
Vol 22 (8) ◽  
pp. 1919-1923
Author(s):  
Jamaiah H Yahaya ◽  
Aziz Deraman ◽  
Nor Hani Zulkifli Abai ◽  
Zulkefli Mansor ◽  
Yusmadi Yah Jusoh
Keyword(s):  
Big Data ◽  
Public Sector ◽  
Conceptual Framework ◽  
Organizational Performance ◽  
Performance Management ◽  
Business Intelligence ◽  
Data Analytics ◽  
Big Data Analytics
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