Business Value Creation Research Articles

Financing mode decision of characteristic tourist town based on improved G1 and Z-number-TOPSIS: the Chinese case

PurposeThe objective is to address financing challenges with an innovative technical approach and provide financial support to facilitate the sustainable development of characteristic tourist towns.Design/methodology/approachIn this study, a novel decision model is proposed, which utilizes the CRITIC improved G1 weighting method to analyze financing factors and applies GRA to enhance the TOPSIS model under a Z-Number fuzzy environment. Finally, sensitivity analysis and comparative assessment were conducted to validate the findings and the model.FindingsThe findings indicate that equity financing is the optimal mode of financing for characteristic tourist towns, with bond financing serving as a viable alternative. Key factors influencing financing include economic benefit, social benefit and policy risk. Managers should carefully consider these factors when selecting financing methods in order to enhance efficiency and mitigate risks.Originality/valueAs a new business model and value creation method of cultural and tourism integration, the financing decision of a characteristic tourism town has always been the key to the overall promotion and operation. The research constructs a financing index system for characteristic tourism towns based on benefit and risk considerations, using the hot spring town in Anhui Province, China, as a case study to evaluate eight financing models.

Review of AI-Driven Solutions in Business Value and Operational Efficiency

Introduction. Artificial intelligence (AI) refers to a wide spectrum of breakthroughs that offer multiple advantages to companies in terms of increased sales. Considering the abundance of data and the significant increase in computational resources, organisations have rapidly turned to artificial intelligence (AI) to create financial benefits. Nevertheless, businesses continue to discover it is challenging to implement and employ AI in their everyday activities. Therefore, a comprehensive understanding is required due to the absence of an integrated comprehension of how artificial intelligence creates business value and what kind of corporate worth is anticipated. Aim and tasks. The study aims to review value-generating methods and explain how enterprises might use AI technology in their business activities. To accomplish its main aims, this study offers a thorough literature review. The working hypothesis claims that the use of AI can increase business value. Results. This study examines the research capabilities of AI, its use in the corporate environment, and its initial and secondary impacts. The impact of AI includes process efficiency, generating insights hidden in huge amounts of data, and transforming business processes in terms of procedural actions, operational efficiency, financial efficiency, market efficiency, and sustainability in terms of company profile. In addition to the favourable impacts, several recent cases have shown that unwanted and undesired consequences may develop in the absence of effective management procedures. These effects hurt the reputation of the businesses concerned and, in certain cases, resulted in huge fines and financial losses. Such findings increase the responsibility of AI enterprises to incorporate solutions that reduce the bias in data and algorithms at every stage of implementation. Conclusions. The role of artificial intelligence in the corporate environment in value creation and operational efficiency is extending. AI technologies can be used by companies to increase automation of corporate processes without direct interaction with customers, including applications that mean the use of AI in customer-facing services and products. Learning about the means by which AI might be employed will assist businesses in generating rational choices regarding the strength of implementing technologies in the supply chain. Assessing the possible implications of AI acceptance of artificial intelligence may enable firms to plan more successfully on a technology’s launch.

Circular Economy Business Models, Value Creation and Humane Entrepreneurship: A Micro-Sized and Social Enterprise Perspective

AbstractThe circular economy is currently championed across different quarters as a potential solution to contemporary and multiple sustainability challenges. Academically, the concept has witnessed a substantial increase in scholars’ interest. Yet the locus of current literature is mainly conceptual, and there is a dearth of contributions from business and management studies. Drawing on the circular business models and the nascent humane entrepreneurship literature, and an exemplary, micro-sized social enterprise, we find that circular business models generate multiple forms of social value beyond the creation of job opportunities only, and that they are characterised by a humane entrepreneurship orientation. As a result, we contribute to the limited literature on circular economy implementation and to the emerging literature at the intersection between entrepreneurship and the circular economy.

How Consulting Firms Leverage Dynamic Capabilities to Create Business Value in the Metaverse

How Consulting Firms Leverage Dynamic Capabilities to Create Business Value in the Metaverse

Towards a process selection method for embedded analytics

AbstractDriven by technological progress, business analytics is gaining momentum while paving the path for next-generation business process management. Especially, embedded real-time analytics offers new opportunities for business process intelligence and value creation. However, there are several obstacles that organizations face in their adoption process. A key challenge is to identify business processes that are suitable for embedded analytics and hold relevant value potential. Our research addresses this need by introducing an exploratory BPM method, namely a process selection method. Applying action design research and situational method engineering, we iteratively built, used, evaluated, and refined the theory-ingrained method artifact. The method provides organizations with guidance in selecting operational business processes, for which a reengineering project should be initiated.

Exploring IT business value creation in a Taobao Village EC ecosystem: A systems theory perspective

By extending and elaborating on Nevo and Wade's systems theory perspective, we propose a model to validate the causal relationships among platform compatibility, integration effort, realized synergy, and IT business value (decomposed into social and economic value) within the context of a Taobao village E-commerce (EC) ecosystem. We collected data from Taobao village EC ecosystems to validate the proposed hypotheses. Using 258 valid responses, we reveal the following: (1) Platform compatibility, a second-order latent construct, includes IT-commerce alignment and resource-sharing routine alignment. Platform compatibility positively affects realized synergy. (2) Integration effort positively affects platform compatibility and realized synergy. (3) Realized synergy positively impacts social and economic value. (4) Realized synergy mediates the relationship between critical enablers (integration effort and platform compatibility) and social and economic value. Specifically, the relationship between platform compatibility and social and economic value is partially mediated, while the relationship between integration effort and value is fully mediated. These findings extend systems theory by offering deeper insights into the integration of systems theory and RBV, particularly highlighting the mediating role of synergy. They reveal that compatibility and integration effort directly contribute to value creation, and that synergy further amplifies these effects.

REAL TIME DATA RETRIEVAL AND CONCURRENT DATA FLOW

Real -Time analytics (RTA) has emerged as a distinct branch of big data analytics focusing on the velocity aspect of big data , in which data is prepared , processed and analyzed as it arrives, intending to generate insight and create business value in near real-time. The software system being produced is called E-Commerce Web. This system is designed to “Provide Real Time data Retrieval & Management” for the process of placing an order on the Internet and facilitating the actual delivery of the product. E-Commerce is now seen as a reality for many businesses and a normal part of a business plan. Different key technologies used and highlights the significant features including real time data update via php, Inventory Management and Security, Employee Management and Attendance Functionality, User Registration and related issues, Payment options, Mobile Functionalities are considered. We proposed a system here consumer moves through the internet to the web site. From there, he decides that he wants to purchase something, so he is moved to the online transaction server, where all of the information he gives is encrypted. Once he has placed his order, the information moves through a private gateway to a Processing Network, where the issuing and acquiring banks complete or deny the transaction. This generally takes place in no more than 5-7seconds. It provides a brief overview of what e-commerce website business is about , its key features and the goals it aims to achieve. Key Words: Web Development, Technologies, E-Commerce, Real-Time Analytics, data streaming, Big data analytics, Data streaming, complex event processing.

Marketing innovation for sustainability: Review, trends, and way forward

AbstractSustainable development goals are aligning marketing innovations to meet sustainability interventions. Recently, marketing has evolved to incorporate sustainability in outreach objectives. Heightened literature on the interplay between sustainability, innovations, and marketing demands a holistic understanding to guide future research direction. The current review bridges the research gap using quantitative performance analysis and qualitative intellectual structure analysis. The thematic and content analysis points towards permeating sustainability focus across the business verticals and value chain for differentiated brand positioning and sustainability‐based competitive advantage. The emergent conceptual framework underpins the moderated‐mediation role of sustainability and economic enablers with marketing initiatives to promote business innovations beneficial to all stakeholders. The sustainability‐aligned innovations in products and processes reflect in gaining cost advantage, revenue generation, access to newer markets, and differentiation. It can be inferred from the study that sustainability and conducive marketing strategies can co‐create value across the value chain, providing tangible and non‐tangible corporate payoffs. This cross‐section in the research domain calls for environmental and business focus at the micro and macro levels, supported by pro‐business strategies, system efficiencies, productivity, and technological changes in synergy with the societal landscape. The findings discern strategic directions for practitioners to chart out an organizational portfolio centered around marketing innovations to create business value for the shareholders and sustainability value for the stakeholders, ecosystem, and society. The conceptual framework can help academicians comprehend the business model around sustainability, innovations, and marketing. The evolutionary mapping of the research domain can be used to discern corporate or macro‐level policies and transcend reforms aligned to the dynamic market/stakeholder expectations.

Creating AI business value through BPM capabilities

PurposeAlthough businesses continue to take up artificial intelligence (AI), concerns remain that companies are not realising the full value of their investments. The study aims to provide insights into how AI creates business value by investigating the mediating role of Business Process Management (BPM) capabilities.Design/methodology/approachThe integrative model of IT Business Value was contextualised, and structural equation modelling was applied to validate the proposed serial multiple mediation model using a sample of 448 organisations based in the EU.FindingsThe results validate the proposed serial multiple mediation model according to which AI adoption increases organisational performance through decision-making and business process performance. Process automation, organisational learning and process innovation are significant complementary partial mediators, thereby shedding light on how AI creates business value.Research limitations/implicationsIn pursuing a complex nomological framework, multiple perspectives on realising business value from AI investments were incorporated. Several moderators presenting complementary organisational resources (e.g. culture, digital maturity, BPM maturity) could be included to identify behaviour in more complex relationships. The ethical and moral issues surrounding AI and its use could also be examined.Practical implicationsThe provided insights can help guide organisations towards the most promising AI activities of process automation with AI-enabled decision-making, organisational learning and process innovation to yield business value.Originality/valueWhile previous research assumed a moderated relationship, this study extends the growing literature on AI business value by empirically investigating a comprehensive nomological network that links AI adoption to organisational performance in a BPM setting.

Key Industry 4.0 Organisational Capability Prioritisation towards Organisational Transformation

Industry 4.0 aids organisational transformation powered by innovative technologies and connectivity. In addition to navigating complex Industry 4.0 concepts and characteristics, organisations must also address organisational consequences related to fast-paced organisational transformation and resource efficacy. The optimal allocation of organisational resources and capabilities to large transformational programs, as well as the significant capital investment associated with digital transformation, compel organisations to prioritize their efforts. Hence, this study investigates how key Industry 4.0 organisational capabilities could be prioritized towards organisational digital transformation. Data were collected from 49 participants who had completed a questionnaire containing 26 statement actions aligned to sensing, seizing, transforming and supporting organisational capability domains. By analysing the data, statement actions were prioritized and operationalized into a prototyped checklist. Two organisations applied the prototyped checklist, illustrating unique profiles and transformative actions. The operationalisation of the checklist highlighted its utility in establishing where an organisation operates in terms of digital transformation, as well as what additional steps might be followed to improve its capability prioritisation based on low checklist scores. By understanding the prioritisation of Industry 4.0 capabilities, organisations could ensure that resources are allocated optimally for business value creation based on organisational capabilities prioritisation.

REVIEWING THE TRANSFORMATIONAL IMPACT OF EDGE COMPUTING ON REAL-TIME DATA PROCESSING AND ANALYTICS

Edge computing has emerged as a pivotal paradigm shift in the realm of data processing and analytics, revolutionizing the way organizations handle real-time data. This review presents a comprehensive review of the transformational impact of edge computing on real-time data processing and analytics. Firstly, the review delves into the fundamental concepts of edge computing, elucidating its architectural framework and highlighting its distinct advantages over traditional cloud-centric approaches. By distributing computational resources closer to data sources, edge computing mitigates latency issues and enhances responsiveness, thereby enabling real-time data processing at the edge. Furthermore, this review explores how edge computing facilitates the seamless integration of analytics capabilities into edge devices, empowering organizations to derive actionable insights at the source of data generation. Leveraging advanced analytics algorithms, such as machine learning and artificial intelligence, edge computing enables autonomous decision-making and predictive analytics in real time, fostering innovation across diverse industry verticals. Moreover, the review examines the transformative implications of edge computing on various sectors, including healthcare, manufacturing, transportation, and smart cities. By enabling localized data processing and analytics, edge computing enhances operational efficiency, ensures data privacy and security, and unlocks new opportunities for business optimization and value creation. This review underscores the profound impact of edge computing on real-time data processing and analytics, revolutionizing the way organizations harness data to drive informed decision-making and gain competitive advantage in today's dynamic business landscape. As edge computing continues to evolve, its transformative potential is poised to redefine the future of data-driven innovation and digital transformation.
 Keywords: Edge, Computing, Analytics, Data, Impact, Review.

Entrepreneurial performance and marketing analytics: the role of new product innovation

PurposePrevious studies focus on the direct effects of marketing analytics on entrepreneurial performance, but few explore the underlying mechanisms. Drawing on affordance theory, this study explores pathways through new product innovation (NPI) for the effects of marketing analytics on business performance. NPI is a market-based innovation concept comprising customer- and competitor-driven NPD and incremental innovation.Design/methodology/approachUsing survey data collected from UK-based entrepreneurial firms operating in the IT and telecoms industries, we apply confirmatory factor analysis and a sequential structural equation model to test the mediating role of NPI in the effect of marketing analytics on market performance and financial performance.FindingsThe results show that marketing analytics enhances business performance through competitor-driven but not customer-driven NPD. Although using marketing analytics to generate customer knowledge for existing product innovation may enhance market performance, this positive effect becomes negative when competitor-driven NPD is undertaken to improve existing product innovation.Originality/valueThis study makes significant contributions to the innovation and NPD literature. It delves deeper into the existing view on the positive contributions of customer engagement to business value creation, revealing the significance of competitor knowledge to enhance business performance through marketing analytics, particularly in the context of IT and telecoms entrepreneurial firms.

Sub-suppliers model 4.0 and knowledge discovery in industry supply chain

Web 4.0 revolution and the abundance of industrial data in the current business environment allow each industrial company to innovate in a diversity of ways. The novelty approaches for the industry are often related to Industry 4.0, where data plays a key role. This research paper aims to develop a new model of the Sub-Supplier process description „Sub-Suppliers Model 4.0“. The model presents the role of knowledge management and Web 4.0 as having a key impact on the processes for business value creation for the industrial organisation. The paper is structured into three chapters. The introduction presents the methods used along with the research question. Chapter one starts with the theoretical bases and state of the art of the Industrial Sub-Supplier Process (ISSP). Chapter two makes a link between the ISSP, knowledge management and knowledge discovery process using data from Supply Chain Management Software for management decision-making. Chapter three of the research starts with web 4.0 tools and proposes a Sub-Suppliers Model 4.0, while trying to explain how the ISSP can be optimized based on the KPIs used. The main research contribution is the novelty integration of the knowledge discovery concept and the implementation of web 4.0 tools into the ISSP.

Exploring the Impacts of Knowledge Infrastructure, Dynamic Capability, and Customer Relationship Management Ability on Business Value of a Firm

The dynamic capability of a firm is the ability to combine, develop, and reconfigure the external as well as internal expertise to respond to the changing business environment in a faster manner. A firm's knowledge infrastructure capability includes structural ability, technological ability, and human resource ability to meet the changing business requirements for the firm to manage its knowledge base. Also, the customer relationship management (CRM) ability of a firm helps it to engage with customers to meet their dynamically changing requirements. Studies show how these capabilities can enhance business value. But there is no study which investigated the combined impacts of a firm's knowledge infrastructure, dynamic capability, and CRM capability on creating business value. Therefore, this study aims to close this gap. With the help of dynamic capability view and resource-based view theory along with literature, we prepared a theoretical model. Later, we validated the model using partial least square structural equation modeling technique with 317 responses from India. The study finds that there is a significant and positive combined impact of a firm's knowledge infrastructure, dynamic capability, and CRM capability on the value creation for the firm, which in turn significantly and positively impacts firm performance.

Moderating Role of Information Technology Knowledge Management on Information Quality and Business Value Creation: Analysis from Commercial Banks in Tanzania

Moderating Role of Information Technology Knowledge Management on Information Quality and Business Value Creation: Analysis from Commercial Banks in Tanzania

Strategic Key Elements in Big Data Analytics as Driving Forces of IoT Manufacturing Value Creation: A Challenge for Research Framework

Big Data (BD)-driven business intelligence (BI) is considered to be a new development stage within industrial engineering management to achieve higher business value creation. The article provides extensive research of BD-driven approaches to identify strategic knowledge and value-creating components within Internet of Things (IoT) manufacturing. Due to previous research studies focused mostly on partial solutions and lack a holistic view, this article topic is still far from being sufficiently explored and resolved. The article presents the results of an extensive research study of contemporary professional literature with a specific aim to provide a more holistic and systematical view on the current state and future trends in this issue. To solve this high complex research problem, both quantitative and qualitative research methodology approaches are used. The article is primarily based on a systematic literature review covering 187 essential and 36 additional current research papers within the Web of Science and Scopus database from 2012 to the present. Research findings identify the seven key strategic elements determining that Big Data analytics (BDA) can become a real and tremendous business value creation driver within IoT intelligent manufacturing. These research results provide important conclusions and implications for challenging future investigations into BDA and BI systems in the context of advancing the Industry 4.0 revolution. In conclusion, we propose a final suggestion in the form of a strategic future research chain from IoT through BDA to value creation based on BD intelligence within IoT manufacturing, whereas seven strategic elements might play a crucial.

Unicage Architecture Development Method

Digital transformation necessitates the creation of business value or innovation using digital technology in every industry. Enterprises are facing an unprecedented business environment and competition for resources and market, which forces them to be more adaptive. Consequently, their Enterprise Information Systems must also be adaptive as so for Architecture Development Method. As a result, enterprises turn to adopt Agile and Lean approaches at scale, which requires architecture iteration and refactoring, or evolvability. In this paper, we propose a loosely-coupled, process-based enterprise architecture development method- Unicage – which aligns with Agile and Lean principles, complementing them with concrete technology architecture. The proposing Architecture Development Method especially fits for Enterprise Resource Planning systems.

Choosing the right path for AI integration in engineering companies: A strategic guide

The Engineering, Procurement and Construction (EPC) businesses operating within the energy sector are recognizing the increasing importance of Artificial Intelligence (AI). Many EPC companies and their clients have realized the benefits of applying AI to their businesses in order to reduce manual work, drive productivity, and streamline future operations of engineered installations in a highly competitive industry. The current AI market offers various solutions and services to support this industry, but organizations must understand how to acquire AI technology in the most beneficial way based on their business strategy and available resources. This paper presents a framework for EPC companies in their transformation towards AI. Our work is based on examples of project execution of AI-based products development at one of the biggest EPC contractors worldwide and on insights from EPC vendor companies already integrating AI into their engineering solutions. The paper covers the entire life cycle of building AI solutions, from initial business understanding to deployment and further evolution. The framework identifies how various factors influence the choice of approach toward AI project development within large international engineering corporations. By presenting a practical guide for optimal approach selection, this paper contributes to the research in AI project management and organizational strategies for integrating AI technology into businesses. The framework might also help engineering companies choose the optimum AI approach to create business value.

Value creation and appropriation from the use of machine learning: a study of start-ups using fuzzy-set qualitative comparative analysis

This study focuses on how start-ups use machine learning technology to create and appropriate value. A firm’s use of machine learning can activate data network effects. These data network effects can then create perceived value for users. This study examines the interaction between the activation of data network effects by start-ups and the value that they are able to create and appropriate based on their business model. A neo-configurational approach built on fuzzy-set qualitative comparative analysis (fsQCA) explores how the design of a firm’s business model interacts with various aspects to explain value creation and appropriation using machine learning. The study uses a sample of 122 European start-ups created between 2019 and 2022. It explores the system of interactions between business model value drivers and value creation factors under the theory of data network effects. The findings show that start-ups primarily activate the efficiency and novelty elements of value creation and value capture.

Translating mission-driven sustainability values into a value-creating business model: The Norwegian start-up Ducky AS

Sustainability entrepreneurs are driven by their personal missions about the sustainability-based values that they wish to tap when building a business model. This teaching-case study presents insights into how the personal sustainability missions and goals of entrepreneurs, such as combating climate change in terms of CO2 emission reductions, can be realised, measured and aligned with concrete customer value in order to build a business model. Here, the case of the company Ducky AS is presented, a Norwegian start-up business that has been working for years to transform the specific sustainability missions of its entrepreneurs into concrete social and business values. The case is analysed through the lens of complexity with regards to sustainability management and entrepreneurship. A qualitative research approach with material stemming from a guest lecture and a subsequent interview is used to study the case.