Data-driven Business Research Articles

Assessing the role of competitive intelligence and practices of dynamic capabilities in business accommodation of SMEs

Dynamic capability is an emerging topic for organizations confronting a chaotic environment. Previous research has emphasized that organizations with low dynamic capacities boost performance suffers from several survival issues. This paper discusses how small and medium-sized enterprises (SMEs) may strengthen their dynamic capabilities using metrics and knowledge management procedures encompassing information planning, collecting, analyzing, and distributing. Specifically, this research explores the influence of social media analytics on four stages of competitive intelligence to strengthen the dynamic capacities inside manufacturing enterprises. Social media analytics is better utilized in some major corporations but less accepted in small and medium enterprises. However, little empirical research has applied the dynamic capabilities method and studied the causal linkages between social media monitoring and data-driven business processes. A survey was undertaken among 150 manufacturing small and medium enterprises in Shanghai to understand this connection better. Consequently, a closed-structured questionnaire was issued to the small and medium enterprises’ chief executive officers and management. The data gathered was examined using structural equation modeling. Our study results suggest that social media analytics favorably influences four stages of competitive intelligence, notably the phases of collecting and analysis.

Data-driven business and data privacy: Challenges and measures for product-based companies

To leverage the opportunities provided by the Internet of Things (IoT), product-based companies are exploring new data-driven business opportunities. They may miss these same opportunities, however, owing to data-privacy challenges. These challenges start with the customers of product-based companies, extend to the wider business ecosystem, and continue with the companies themselves. This article identifies 12 data-privacy challenges and introduces 12 measures to address them. These include intuitive recommendations, such as enabling cross-product consent collection, as well as less intuitive measures, such as fostering a can-do attitude in legal units, closing the gap between legal and business initiatives, or implementing a clear process for well-reasoned risk-taking. The following four principles were found to support companies in implementing these measures: (1) letting privacy and data-driven business go hand in hand, (2) putting customers first and turning their privacy preferences into opportunities, (3) aligning risk-management activities with the process of digital service development, and (4) using technology to professionalize legal processes.

Construction Mechanism of Data-driven New Retail Business Model Loop: A Case Study of Hema

As the development driving force and new production factor in the era of digital economy, data plays an important role in new retail business model innovation that begins to show the characteristic of closed loop, a term we define as data-driven new retail business model loop, however, we know little about this process and the mechanism behind. In this paper, grounded in an in-depth case study of Hema, a fresh new retail enterprise in China, we systematically explore the construction mechanism of data-driven new retail business model loop through data network effects theory. The findings reveal that the construction of data-driven new retail business model loop includes three phases, namely start, expansion, and reinforcement, they are manifested in the dynamic cycle of “experience optimization of all touchpoints ? value network expansion ? competitive advantage realization”, with the boosting logic of data network effects behind. Specifically, according to the data network effects theory, start, expansion, and reinforcement three phases can be respectively refined as “scene matching”, “connection synergy”, and “value-adding efficiency”, the boosting logic of data network effects theory behind the construction of data-driven new retail business model loop can be realized through the dynamic cycle of “scene matching ? connection synergy ? value-adding efficiency”. The final theoretical model built in this paper can not only contribute to the existing research gaps on the loop innovation of data-driven business model and new retail business model, but also expand data network effects by its theoretical boundary and research context.

New geographies of platform capitalism: The case of digital monopolization in Turkey

The rise of digital platforms is growingly acknowledged as a pivotal characteristic of contemporary capitalism. A subset of the literature on digital platforms scrutinized the political economy of platform companies, their data-driven business models along with the economic, social, and political consequences of platformization. Yet, this strand has been overwhelmingly interested in Big Tech companies in the United States and China. In this article, I aim to expand the geographical span of this emerging literature by scrutinizing platformization dynamics in Turkey, a middle-income country that has not been systematically examined so far along these lines. To this end, I present an assessment on the overall platform landscape in Turkey by focusing on three highlight digital platforms (Trendyol, Hepsiburada, and Getir). My analysis on the trajectory of these three platforms indicates the following. First, these platforms have exhibited meteoric growth rates and have risen among the most valuable companies in Turkey in a tremendously short period of time. Second, their aggressive growth strategies based on data extraction and network externalities have led to a strong monopolization drive. Third, they have rapidly expanded into adjacent business areas, transforming themselves into infrastructural components of the domestic economy, with profound impacts on production, trade, and labor relations. Fourth, they remain dependent on the infrastructure laid by Big Techs. These findings suggest that platformization is rapidly transforming economic landscapes other than the United States and China as well, yet in an uneven and dependent form, underlining the need to broaden the geographical focus of the concerned literature.

Optimizing firm's supply chain resilience in data-driven business environment

PurposeThis study aims to investigate the impact of a firm’s supply chain capabilities on supply chain resilience, and the impact of supply chain resilience on sustainable supply chain performance in a data-driven business environment. The study also aims to explore the function of supply chain resilience in mediating the relationship between a firm’s supply chain capability and sustainable supply chain performance.Design/methodology/approachPrimary data were acquired through a survey of 310 managers of small- and medium-sized businesses in a variety of industries across Bangladesh. The data were analyzed using partial least squares structural equation modeling.FindingsA firm’s supply chain capabilities include information technology, leadership and collaboration. Supply chain capability is positively associated with supply chain resilience. The resilience of a firm’s supply chain is also positively correlated with its sustainable supply chain performance. Supply chain resilience plays a mediating role in the relationship between a firm’s supply chain capabilities and its sustainable supply chain performance.Research limitations/implicationsThis study provides a theoretical contribution by corroborating practical knowledge focusing on firms’ supply chain capability, supply chain resilience and sustainable supply chain performance by using a resource-based view and dynamic capability theory – a relevant and unexplored subject in the supply chain literature – and proposes several opportunities for future research.Practical implicationsThe results highlight the study’s managerial and social relevance from the perspective of firms in developing countries. As firms shift toward an online environment, managers and decision-makers need to make strategic decisions, as they did to overcome the challenges presented by COVID-19.Originality/valueThe study’s findings demonstrate that firms’ supply chain capabilities can be leveraged to increase supply chain resilience. Firms’ resilience during COVID-19 allowed them to avoid losses and to improve their supply chain’s sustainable performance. To the best of the authors’ knowledge, their complex higher order model is a unique contribution to the literature on firms’ supply chain capability and extends previous research on this topic.

The D-BEST Reference Model: A Flexible and Sustainable Support for the Digital Transformation of Small and Medium Enterprises

Nowadays, to remain competitive, manufacturing companies must address the Industry 4.0 paradigm, particularly the cyber-physical system (CPS) revolution, following sustainable challenges. Digital innovation hubs (DIHs), as ecosystems that provide technical knowhow, experimental skills, and specialist knowledge, are progressively gaining a strategic role in supporting such a transition, especially in European small and medium enterprises (SMEs). Several projects have been funded by the European Commission to support the growth and action of DIHs. However, which among the four main functionalities (networking, skills and training, test before investing, and access to funding) each DIH is capable of addressing in its daily support action to companies is still unclear. Thus, it is important to configure the service portfolios of DIHs (i.e. the set of services that each DIH can provide based on its characterising assets, knowledge, and capabilities). In this paper, the data-driven business–ecosystem–skills–technology (D-BEST) reference model is proposed and then tested through a survey. The model triggers the identification and materialisation of service-based collaboration processes among DIHs based on their service portfolio analysis and supports the modelling of Collaborative Networks 4.0, in which DIHs are a strategic player because of their ‘by-design’ innovation characteristics. Finally, through the extended adoption of the models developed by different emerging DIH networks, flexibility and interoperability can be reached, fostering the adoption of a unique digital platform to showcase and offer assets, triggering the creation of multiple inter- and intra-communications and collaborations among stakeholders belonging to the DIH ecosystems, and favouring the exchange and development of joint services.

Role of Leadership in Adoption of Blockchain Technology in Small and Medium Enterprises in Saudi Arabia.

This research aims to determine the role of the leadership and potential benefits that blockchain adoption may bring to SME financing in the Kingdom, as well as the foreseeable challenges that may hinder small businesses from benefiting from the adoption of blockchain. It is interesting aspect to see how leadership manages to adopt new changes amid several challenges and threats. This article also outlines policy and regulatory trends that SMEs can save operating costs and improve efficiency, thereby increasing transparency and easier access to funds. Digital technology and creative business models have the potential to help narrow the financing gap for SMEs. E-commerce and the sharing economy provide SMEs with more market access and new business models, as well as the data-driven business prospects generated through data sharing under the framework of open banking. This study provides recommendation that there is a dire need to pay attention on the complete mechanism of the SME’s in order to support them as well as promote them to show their distinction in the contribution of social and economic development. This study provides implications for the financial institutions, government agencies and society to come forward equally for the common interest.

How Can Interactive Process Discovery Address Data Quality Issues in Real Business Settings? Evidence from a Case Study in Healthcare

The focus of this paper is on how data quality can affect business process discovery in real complex environments, which is a major factor determining the success in any data-driven Business Process Management project. Many real-life event logs, especially healthcare ones, can suffer from several data quality issues, some of which cannot be solved by pre-processing or data cleaning techniques, leading to inaccurate results. We take an innovative Process Mining (PM) approach, termed Interactive Process Discovery (IPD), which combines domain knowledge with available data. This approach can overcome the limitations of noisy and incomplete event logs by putting “humans in the loop”, leading to improved business process modelling. This is particularly valuable in healthcare, where physicians have a tacit domain knowledge not available in the event log, and, thus, difficult to elicit. We conducted a two-step approach based on a controlled experiment and a case study in an Italian hospital. At each step, we compared IPD with traditional PM techniques to assess the extent to which domain knowledge helps to improve the accuracy of process models. The case study tests the effectiveness of IPD to uncover knowledge-intensive processes extracted from noisy real-life event logs. The evaluation has been carried out by exploiting a real dataset of an Italian hospital, involving the medical staff. IPD can produce an accurate process model that is fully compliant with the clinical guidelines by addressing data quality issues. Accurate and reliable process models can support healthcare organizations in detecting process-related issues and in taking decisions related to capacity planning and process re-design.

FRAMEWORK TO BUILD AN ADVANCED ANALYTICS MATURITY ASSESSMENT MODEL: QUESTIONNAIRE DESIGN

INTRODUCTION
 Every minute of every day, a huge amount of data is created – social media, email communication, any device connected to the internet, google search. The future of digitalisation and internet of things promises a further generation of new data volumes. New advanced analytical approaches are required to deal with and make sense of large volumes of unstructured and structured data.
 Data-driven business environment is a competitive advantage for any organisation. To ensure faster and smarter decision-making, organisations are forced to use advanced analytics to analyse the past, understand the present behaviour and predict and influence future events, actions, decisions and behaviour. Assessment of the advanced analytics ecosystem is crucial for further development, competitions in the market and to reach the strategic goals of the organisation. The assessment and understanding of the investments needed and next steps is critical to make the digitalisation process productive.
 Advanced analytics can be described as a process of turning huge volumes of structured or unstructured data, statistical and predictive analytics into decision-making with a value to business. In addition, time after time it is accepted as predictive analytics, big data analytics, data mining and similar. This is a forward-looking technique that can provide insights from huge unstructured or structured data volumes. The techniques used include data mining, machine learning, forecasting, visualisation, semantic analysis, sentiment analysis, network and cluster analysis, multivariate statistics, graph analysis, simulation, neural networks and others.
 MATERIALS AND METHODS
 The questionnaire developed by the author is based on an academic literature review, reports and publications shared by the analytics sector, industry experts and the author’s professional experience in the advanced analytics industry. The questionnaire design is developed based on 4 models with disclosed or semi-disclosed information to obtain data for modelling: 1) Analytics Maturity Quotient Framework (AMQ), Authors: Aryng, Data Science consulting, training and advising company; 2) DELTA Plus Model, Authors: International Institute for Analytics (IIA, Davenport); 3) Defining analytics maturity indicators (DAMI), Authors: Jasmien Lismont, Jan Vanthienen, Bart Baesens, Wilfried Lemahieu; 4) TDWI Analytics Maturity Model, Authors: TDWI (Transforming data with intelligence) training company.
 RESULTS
 The outcome is a questionnaire with 40 questions – single choice, multiple choice, text entry, matrix table, constant sum type questions allowing one to collect wide ranging and structured information. The main blocks are Demographics, Data management, Analytics, Process around data and analytics, People, Technologies, Culture, Leadership, Success drivers, Barriers. The questionnaire was launched on 20 December 2021 and now the field work is at the final stage. The author developed the new questionnaire, adjusted it for 2022, and localised it for Latvia.
 DISCUSSION
 Considering the increasing demand for advanced analytics including automated decision-making based on data, the significance of understanding the advanced analytics ecosystem maturity level in the organisations of Latvia is topical. The outcome is a core phase of the research paper “Challenges of Advanced Analytics Adoption in the Organisations of Latvia” with the research goal to develop the Advanced Analytics Ecosystem Assessment and Recommendation Tool based on ‘self-assessment’ to improve the results of the organisation in accordance with the strategic goals of the organisation. The questionnaire design of the quantitative survey is the most significant step in building an assessment model that allows one to collect core data material to obtain the initial model. The domains and factors which determine the maturity of advanced analytics will be identified based on quantitative survey data using proper analysis such as clustering, factor analysis, correlation analysis and other relevant analytical methods.
 CONCLUSIONS
 The challenge was to create a questionnaire in Latvian because of missing relevant terminology in Latvian, thus, leading to the identification of potential new terminology to be developed and implemented in Latvian.

Peer-to-peer electricity trading and the sharing economy: social, markets and regulatory perspectives

ABSTRACT Peer-to-peer (P2P) electricity trading is a new data-driven business model currently being trialed within the energy sector. Introducing P2P transactions to an essential service such as energy supply could have far-reaching implications for individuals and the grid. This paper raises considerations and questions from social, market design and regulatory points of view, which should be understood and addressed by societies and policymakers. It does this by considering under what circumstances it is reasonable to conceptualize P2P electricity trading as part of the sharing economy, and drawing parallels to the sharing economy experience in other sectors. In order to reap the full societal benefits, while avoiding considerable risks to infrastructure and individuals, a policy approach promoting dialogue and innovation is necessary. We suggest the regulatory sandbox is the most appropriate tool to achieve this and would help avoid the breakdown of trust between policymakers and platform companies observed in other sectors.

Employee perspectives on value realization from data within data-driven business models

Firms are innovating data-driven business models (DDBMs) to realize value from data. Yet, making DDBMs work is challenging, and DDBMs often fall short of expected value realization. One reason for this shortfall is that firms do not know how employees, who decisively influence a DDBM’s value realization, view this complex and multi-facetted topic. We think it is necessary to understand the employees’ perspectives, the dimensions that build these perspectives and the characteristics employees are particularly interested in regarding value realization from data. We address this research gap by applying the Q-methodology to examine the perspectives among 70 employees across twelve DDBMs at a German automotive manufacturer. This yields eight perspectives, e.g., data advocacy, data caution or data practical. By exploring these perspectives, we provide a first groundwork on how employees view and appraise value realization from data which adds to the strive for mastering value realization from data within DDBMs.

Driving or driven by others? A dynamic perspective on how data-driven start-ups strategize across different network roles in digitalized business networks

Digitalization transforms how actors conduct activities and exchange resources in business networks. Data-driven start-ups are key actors in this transformation as they are often the first to commercialize novel digital technologies and influence business networks through their strategizing. Extant Industrial Marketing & Purchasing (IMP) literature has investigated roles and strategizing in business networks. However, understanding how data-driven start-ups' specificities impact their roles and strategizing is still at its infancy. Specifically, the authors conceptually show that assumptions on how data-driven start-ups strategize contradict strategizing tenets of prior IMP literature. Therefore, previous knowledge on strategizing needs further differentiation. Addressing this research need, theories-in-use and multiple case study research on 23 data-driven start-ups is conducted to examine (1) in what network roles they operate and (2) how they strategize within and across different network roles. The study extends IMP and data-driven business model research by identifying four network roles (enabler, extender, transformer, orchestrator) and developing a specific classification framework for data-driven start-ups. Furthermore, the study shows that IMP tenets apply in specific network roles only to some extent to data-driven start-ups. Finally, three strategizing trajectories are identified, which provide a dynamic perspective to IMP literature for guiding entrepreneurs on strategizing opportunities.

Data-driven business process management-based development of Industry 4.0 solutions

• A guideline has been formulated for managing business processes in the field of I4.0. • BPM life cycle of I4.0 technologies should utilize data-driven development tools. • Business Process Management (BPM) can be improved by data-driven tools and methods. • Decision support tools, data analytics systems and standards should be integrated. • The CRISP-DM/ML standards improve the BPM life cycles of Industry 4.0 solutions. Business process management (BPM) supports the management and transformation of organizational operations. This paper provides a structured guideline for improving data-based process development within the BPM life cycle. We show how Industry 4.0-induced tools and models can be integrated within the BPM life cycle to achieve more efficient process excellence and evidence-based decision-making. The paper demonstrates how standards of machine learning (CRISP-ML(Q)), BPM, and tools of design science research can support the redesign phases of Industry 4.0 development. The proposed methodology is carried out on an assembly company, where the proposed improvement steps are investigated by simulation and evaluated by relevant key performance indicators.

Project management in the context of digital transformation of the economy for the sustainable development of Kazakhstan

The Fourth Industrial Revolution and the introduction of the Industry 4.0 innova-tions create a unique opportunity for enterprises to increase productivity, opera-tional efficiency and rational use of resources, scale up successful pilot projects and acquire qualitatively new expertise for employees. The current stage of mod-ernisation of production can provide a significant impetus for economic growth and is a priority for governments and business leaders. Therefore, it is important to study project management strategies in the context of digital transformation in order to implement them in the enterprises of Kazakhstan. The purpose of the pa-per is to investigate the features and specifics of project management in modern conditions and search for methods of sustainable development of the state. The leading research method was the analysis, which made it possible to identify global trends in the field of project management for the implementation of innovations, as well as to assess the current state of enterprises in Kazakhstan after the Gov-ernment of the Republic of Kazakhstan approved the State Program "Digital Ka-zakhstan". As a result of the study, the features of project management for creat-ing a platform for the implementation of the Industry 4.0 for sustainable devel-opment of the state were identified. The authors concluded that the knowledge in-tensive business services need a deep understanding of user preferences and needs. Data-driven business models are the entry point for the digital transformation of enterprises.

Data-driven Business Model Innovation for 6G

Futuristic disruptive technologies like 6G, could further transform the way we live and work, beyond what was enabled by 5G. The expected features could enable massive high-speed and low-latency connectivity across diverse devices giving rise to new applications enabling instant availability of data related to Smart Cities, Industries 4.0, Intelligent Transport Systems, Utilities, Healthcare, Security, Media & Entertainment, etc. They will further make it possible to scale current real-time data collection strategies, adding more devices without the need for wired connections or fear of interference. Data is considered as the new oil due to tremendous possibilities it can bring in the business world through predictive and prescriptive analytics. This gives rise to the importance of data-driven business model innovation to provide actionable insights to the business owners, helping them to develop sustainable business models. Organizations using big data and analytics within their innovation process are more likely to remain competitive and sustainable. This paper discusses the importance of harnessing “Real-time Data” to create value, the challenges in harnessing value from data especially “dark data,” the role of “Data-driven Business Model Innovation” in creating sustainable business and Intelligent Transport System Data-driven Business Model Innovation as an example.

Patterns of business model innovation for advancing IoT platforms

PurposeThe emergence of Internet of Things (IoT) platforms in product companies opens up new data-driven business opportunities. This paper looks at the emergence of these IoT platforms from a business-model perspective.Design/methodology/approachThe study applies a mixed method with two research studies: Study I–a cluster analysis based on a quantitative survey, and Study II–case studies based on qualitative interviews.FindingsThe findings reveal that there is no gradual shift in a company's business model, but in fact three distinct and sequential patterns of business model innovations: (1) platform skimming, (2) platform revenue generation and (3) platform orchestration.Research limitations/implicationsThe results are subject to the typical limitations of both quantitative and qualitative studies.Practical implicationsThe results provide guidance to managers on how to modify the components of the business model (value proposition, value creation and/or delivery and profit equation) in order to enable platforms to advance.Social implicationsAs IoT platforms continue to advance, product companies achieve better performance in terms of productivity and profitability, and more easily secure competitive advantages and jobs.Originality/valueThe paper makes three original contributions: (1) it is the first quantitative study on IoT platforms in product companies, (2) identifies three patterns of business model innovations and (3) offers a first process perspective for understanding the sequence of these patterns as IoT platforms advance.

Privacy of European Citizens in the Face of the Development of New Data- Driven Business Models

The main objective of the paper is to identify the imbalance between the right to privacy and the business objectives of entities creating new Data-Driven Business Models (DDBMs) of consumers (EU citizens). Information about the consumer and their characteristics has nowadays become a service or market commodity thanks to which new economic processes, based on the use of advanced data processing technologies, are created. In digital space, new types of DDBM are established, which provide entrepreneurs with added value, based on the mass use of the consumer’s data collected often without their knowledge, on the margins of legality. This paper analyzes the impact of the development of DDBMs on selected privacy areas: personal data, the right to be forgotten, confidentiality of communications, one’s image and identity. In each of these areas, situations are identified that indicate a progressive re-evaluation of citizens’ privacy rights. The authors suggest that disruption of the balance between the right to privacy and business objectives may lead to unambiguous consequences, not only for the consumer (EU citizen), but also for the business entities.

A Systematic Review of the Literature on Customer Analytics to Predict Dynamic Purchase behavior of Consumers through Credit Cards

Abstract: The Credit card industry is flooded with customer data and also their diversified purchasing behavior. These multifariousness analysis helps different industry to maximize revenue through data and to identify right cluster of audience based on the products and services they offer. To analyse this data efficiently and erect efficient co-relation and streamline process execution, expertise in customer segmentation specifically through customer analytics, is required. This domain of analytics uses a wide variety of concepts related to data mining, Passive and active data collection, Media planning, Regression analysis, Customer based corporate-valuation, Market-structure, Probability Models, Optimization, visualization and Implementation of model spreadsheet for predictions on data set. Moreover, customer analytics is the subset of a larger range of business analytics techniques used for better statistical and predictive analysis to transform and make smarter, data-driven business decisions. Keywords: Regression analysis, Market-structure, Optimization, Business analytics, Model spreadsheet.

A Bibliometric Analysis of Research on Big Data and Its Potential to Value Creation and Capture

The emergence of big data is a radical shift in the business context, leading to a change in value creation and capture. This phenomenon is a newborn concept in the business and management literature confirmed by the growing number of publications over recent years. This paper presents an updating comprehensive bibliometric analysis to describe and assess the scientific landscape of value creation and capture based on leveraging big data in the literature. Bibliometrix and VOSviewer were selected as software tools for descriptive and network bibliometric analysis based on the Web of Science Core Collection database from 2011 till 2020. By implementing bibliometric analysis such as analysis of citations and co-occurrence of keywords, we have recognized the most prominent and influential authors, papers, journals, countries, and four potential clusters of current trends in studies. These four trends of value creation and capture from big data studies are: 1) strengthening the basic knowledge of value creation in the big data era, 2) data-driven business model and value capturing, 3) dynamic capabilities and centrality of knowledge, and 4) digital transformation of the service industry. Finally, by identifying the existing research gaps, future research directions in each cluster are demonstrated.

IoT Blockchain Data Veracity with Data Loss Tolerance

Recent years have witnessed the advancement of the Internet of Things (IoT) and its emergence as a technology that could revolutionize many businesses. It helps considerably in creating data-driven business models with the insights it provides. IoT systems are deployed in data collection, monitor processes, provide insights and allow businesses to make data-driven productivity improvements. However, IoT systems are often experiencing data loss due to inevitable failures ranging from devices, networks, to the application layer, especially in scarce infrastructure resources environments. Data loss might be unrecoverable in many circumstances. As such, this research presents a blockchain based IoT model (framework) with the aim of circumventing data loss. We envisioned IoT blockchain technology in enhancing data veracity with data loss tolerance. That is, to have blockchain enhancing the IoT data veracity by leveraging on the features existed in its peer-to-peer network (P2P) and distributed ledger storage technology (DLT). Additionally, the edge computing of IoT blockchain technology is also conceptually workable; with intelligent small computing resources, it opens up a new era of bringing the intelligence of data collection, connectivity, computation and storage into the edge/device layer. A novel IoT blockchain strength monitoring system is also been studied to further enhance data veracity; this is achieved through a capacitance monitoring on the IoT blockchain system. The empirical results show that the proposed IoT blockchain with a strength monitoring model can alleviate data loss and thus enhance data veracity with data loss tolerance.