Generate Business Value Research Articles

Exploring factors influencing blockchain adoption’s effectiveness in organizations for generating business value: a systematic literature review and thematic analysis

ABSTRACT This paper explores the factors that facilitate and impede blockchain technology adoption (BTA) within business organizations. Analysing 112 scholarly articles via thematic and bibliometric methods reveals a complex adoption landscape, highlighted by the proposition of an innovative conceptual framework that augments the conventional technology-organization-environment (TOE) framework. This augmented framework integrates enabling factors, such as enhanced security mechanisms via smart contracts, and barriers, including scalability challenges and regulatory limitations. The research offers significant insights into cultivating a conducive ecosystem for BTA within organizations and provides practical implications.

Strategic knowledge, IT capabilities and innovation ambidexterity: role of business process performance

PurposeThe research aims to identify the impacts of strategic knowledge (SK) and information technology capabilities (ITC) on innovation ambidexterity (IAM) through business process performance (BPP).Design/methodology/approachThe research framework is developed based on the theoretical grounding of resource orchestration (RO) (SK and ITC) impacts on IAM. The structural equation modeling (SEM) technique was used to test the research framework on a sample of 441 responses from Brazilian firms.FindingsThe results suggest that SK and ITC facilitate BPP, resulting in IAM. The findings also suggested differences in path coefficients in the SK and ITC of the business value generation process framework under environmental turbulence (ET). Finally, a strong SK of ITC is especially important in enabling BPP and IAM in large firms. Another case of most manufacturing and service firms demonstrated that both SK and ITC are essential to impacting IAM through BPP mediation.Practical implicationsThe findings provide insight into how professionals can think and plan carefully to align SK and ITC for achieving balanced innovation and improving BPP in the dynamic business environment.Originality/valueThe study establishes a relationship between SK, ITC, BPP and IAM. The study developed novel constructs of SK and ITC and tested them, which gives new insight and links among the constructs.

HR analytics-as-practice: a systematic literature review

PurposeHuman resource analytics (HRA) is an HR activity that companies and academics increasingly pay attention to. Existing literature conceptualises HRA mostly from an objectivist perspective, which limits understanding of actual HRA activities in the complex organisational environment. This paper therefore draws on the practice-based approach, using a novel framework to conceptualise HRA-as-practice.Design/methodology/approachThe authors conducted a systematic literature review of 100 academic and practitioner-oriented publications to analyse existing HRA literature in relation to practice theory, using the “HRA-as-practice” frame.FindingsThe authors identify the main practices involved in HRA, by whom and how these practices are enacted, and reveal three topics in nomological network of HRA-as-practice: HRA technology, HRA outcomes and HRA hindrances and facilitators, which the authors suggest might actualize enactment of HRA practices.Practical implicationsThe authors offer HR function and HR professionals a basic ground to evaluate HRA as a highly contextual activity that can potentially generate business value and increase HR impact when seen as a complex interaction between HRA practices, HRA practitioners and HRA praxis. The findings also help HR practitioners understand multiple factors that influence the practice of HRA.Originality/valueThis systematic review differs from the previous reviews in two ways. First, it analyses both academic and practitioner-oriented publications. Second, it provides a novel theoretical contribution by conceptualising HRA-as-practice and comprehensively compiling scattered topics and themes related to HRA.

The Agile Knowledge Projector: enhancing the adaptive creation of knowledge

Agile is knowledge-sensitive. Knowledge feeds the entire adaptive life cycle from envision to transition. Main agile frameworks (i.e., SAFe®), toolkits (i.e., DA® – Disciplined Agile) and standards (i.e., PMBOK® 7th Edition) underline in different ways and at different levels of detail the relevance of knowledge for generating business value. So, all right? Everything is fine? Are we on the same page? In principle yes, but there are three main knowledge lacks: implicit knowledge, knowledge framework, knowledge roles/responsibilities. Therefore, negative effects are determined at different ontological levels: project performance, team dynamics, personal development. Presently, the agile landscape shows a relevant awareness of the topic of knowledge. However, this awareness is fragmented, ambiguous in key concepts, poorly addressed, even misleading in some cases. Knowledge in the agile environment is too often a fuzzy topic. The self-dimension of an agile team (self-directing, self- organizing, self-learning, self-empowering) erroneously assumes that knowledge is well addressed, wasting time, money, resources, decreasing performance and results. This paper provides a practical framework and tips for avoiding these negative effects. The AKP® – Agile Knowledge Projector might be a practical solution for better addressing knowledge in an agile project environment, improving value. AKP® is an organizational framework that fosters the creation of a dynamic mix of knowledge, combining in a continuous way explicit organizational artifacts, implicit team patterns and tacit individual expertise, for the entire duration of the agile project. AKP® consists of nine processes, focused on main agile dynamics of a complex and innovative project. AKP® is a powerful but not self-regulated framework: thus, it needs to be carefully designed and administered, according to specific knowledge roles and rules.

New Leadership Models for the Digital and Entrepreneurial Society – Recovering from COVID-19 in an Increasingly Digital Economy

Abstract Leadership for the digital and entrepreneurial society is a relatively new and emerging area of study, as the rapid pace of technological change has brought new challenges and opportunities for leaders. Forasmuch as digital society continues to evolve and grow, and as COVID-19 has had a significant impact on leadership practices and has brought about some notable changes in how leaders approach their roles, new leadership models are emerging to meet the unique provocations and chances of this context. The scientific literature on this specific topic is still emerging, but some key themes have already come up emphasizing the importance of agility and adaptability as organizations have to rapidly pivot their operations in response to the increasingly necessary digital expansion accelerated by the pandemic. As traditional business models are disrupted, leaders who are able to effectively leverage new technologies while thinking creatively and taking risks are more likely to succeed. Therefore, considering that the subject is still in its early stages, the research topic is to examine the leadership models that should be utilized in order to improve the digital and entrepreneurial context answering the main research questions related to the steps and leadership practices that will make a significant difference in both digital and entrepreneurial societies. The past literature on empirical and theoretical topics is compiled via a systematic review. An analysis of the content that is qualitative and descriptive was planned. Scopus, Web of Science or Google Scholar search results were of great help in carrying out the study and various leadership models were examined. The paper’s theoretical contribution is the definition of a set of rules and criteria on where to focus on leadership. The collection of data for new performance, ownership, efficiency, and business value generation indicators is promoted at all levels.

IT impact on open innovation performance: Insights from a large-scale empirical investigation

Motivated by prior inconclusive findings on the effectiveness of open innovation initiatives in generating business value and the complexity of open innovation, this study draws on the IT-enabled organizational capabilities perspective to propose that the mobilization of information technology (IT) resources can positively affect the development of open innovation initiatives and, in turn, improve firms' open innovation performance. Our empirical analysis of >1100 large Spanish firms confirms that firms' IT resource investments positively impact the development and execution of open innovation initiatives, which in turn improves firms' open innovation performance in terms of product and process innovations. This research contributes to the business value of IT and to Information Systems (IS) research on open innovation by evidencing the enabling role of IT investment in achieving open innovation performance gains.

The Impact of Artificial Intelligence on Business Processes

Purpose: The purpose of the study is to examine the challenges faced by businesses in integrating and effectively utilizing artificial intelligence (AI) technology. It aims to provide a comprehensive understanding of how AI technologies generate business value and the anticipated benefits they offer. The study also seeks to identify the facilitators and inhibitors of AI adoption and usage, explore different types of AI use in the organizational environment, and analyze their first- and second-order impacts.
 Methodology: The study employed the comprehensive literature review research design. The researchers conducted a systematic search using predefined criteria in databases such as Scopus and Web of Science. The search yielded 21 relevant papers that were analyzed and synthesized for this study. The data collection method relied on the examination of existing literature. Data analysis involved identifying key themes, trends, and insights from the selected papers. The researchers conducted a qualitative analysis to extract relevant findings and synthesized the information to derive meaningful conclusions.
 Findings: The study revealed several insights regarding the integration and use of AI in businesses. This indicated that organizations struggle with understanding how AI technologies can generate value and how to effectively incorporate them into their operations. Lack of comprehensive knowledge about AI and its value generation processes was identified as a major barrier. Additionally, the study highlighted the facilitators and inhibitors of AI adoption and usage. It identified various types of AI applications in the organizational environment and explored their impacts on business operations. The findings shed light on the challenges businesses face in leveraging AI technology and suggested areas for further research.
 Recommendations: To practitioners: The study emphasizes the importance of acquiring comprehensive knowledge about AI technologies and their potential value generation processes. To policy makers: The study highlights the need for supportive policies and regulations to foster AI adoption. It suggests creating an enabling environment that promotes AI research and development. Theory and Validation: The study may have been informed by existing theories related to AI adoption, organizational change, or innovation. Practice: To practitioners, the study underscores the importance of understanding the value and potential of AI technologies. Policy: To policy makers, the study emphasizes the need for policy frameworks that promote AI adoption and address associated challenges.

Capability Development to Generate Business Value Through Customer-centric Analytics in the Banking Industry: A Systematic Review

Capability Development to Generate Business Value Through Customer-centric Analytics in the Banking Industry: A Systematic Review

Design and analysis of management platform based on financial big data.

Traditional financial accounting will become limited by new technologies which are unable to meet the market development. In order to make financial big data generate business value and improve the information application level of financial management, aiming at the high error rate of current financial data classification system, this article adopts the fuzzy clustering algorithm to classify financial data automatically, and adopts the local outlier factor algorithm with neighborhood relation (NLOF) to detect abnormal data. In addition, a financial data management platform based on distributed Hadoop architecture is designed, which combines MapReduce framework with the fuzzy clustering algorithm and the local outlier factor (LOF) algorithm, and uses MapReduce to operate in parallel with the two algorithms, thus improving the performance of the algorithm and the accuracy of the algorithm, and helping to improve the operational efficiency of enterprise financial data processing. The comparative experimental results show that the proposed platform can achieve the best the running efficiency and the accuracy of financial data classification compared with other methods, which illustrate the effectiveness and superiority of the proposed platform.

Gaining a Competitive Advantage Through Benefits Management

To gain competitive advantage, organizations need to have something that the competitors do not have and cannot achieve in the short-term. In the past, organizations invested large amounts of financial resources to the finest equipment to increase competitiveness. Today, the paradigm has changed, and so the quest is more knowledge- and innovation-driven, where the answer relies on the people's capacity to create and modify processes and generate business value. The investments in information systems and technology (IS/IT) have not always generated the business value or the financial revenue that should be expected. Benefits management focuses on how business areas will improve from business changes and provides a framework for identifying, planning, monitoring, evaluating, and actively managing these benefits. In this paper, the authors show how benefits management reinforces firms to identify more clearly the path to obtain the strategic objectives and the related benefits promoting the organizational competitive advantage.

COSIBAS Platform—Cognitive Services for IoT-Based Scenarios: Application in P2P Networks for Energy Exchange

The revolution generated by the Internet of Things (IoT) has radically changed the world; countless objects with remote sensing, actuation, analysis and sharing capabilities are interconnected over heterogeneous communication networks. Consequently, all of today's devices can connect to the internet and can provide valuable information for decision making. However, the data collected by different devices are in different formats, which makes it necessary to develop a solution that integrates comprehensive semantic tools to represent, integrate and acquire knowledge, which is a major challenge for IoT environments. The proposed solution addresses this challenge by using IoT semantic data to reason about actionable knowledge, combining next-generation semantic technologies and artificial intelligence through a set of cognitive components that enables easy interoperability and integration for both legacy systems and emerging technologies, such as IoT, to generate business value in terms of faster analytics and improved decision making. Thus, combining IoT environments with cognitive artificial intelligence services, COSIBAS builds an abstraction layer between existing platforms for IoT and AI technologies to enable cognitive solutions and increase interoperability across multiple domains. The resulting low-cost cross platform supports scalability and the evolution of large-scale heterogeneous systems and allows the modernization of legacy infrastructures with cognitive tools and communication mechanisms while reusing assets.

AI in Healthcare Management and Accounting: Novelties and Trends from a Literature Review and Illustrative Cases

Through a mixed-methods approach based on a critical analysis of both literature and industry trends (referred to real-world practices / illustrative cases), as rapidly emerging in recent years, we investigate the transformative impact of Artificial Intelligence on healthcare sector, notably the powerful intersection – still too little explored by extant scientific doctrine – of health management and accounting system AI related. As the technological advancements continue to reshape the healthcare landscape, we found an AI expanding as a pivotal instrument in managing, innovating, and optimising strategic and operational processes (by enhancing executives’ decision-making). We drew as main effect of such a process – when enabled by a fruitful integration of managers and experts (data scientists / IT professionals) – an improvement of both patient care and financial efficiency as well (i.e., overall healthcare delivery in order to generate business value) thanks to a more profound real-time KPIs monitoring (patient outcomes, resource utilisation, and financial metrics). We also critically review new challenges such as data privacy, security, and ethical considerations emphasising the importance of responsible AI deployment for building patient trust and ensuring regulatory compliance. In this regard, a public awareness of AI and the establishment of standardised guidelines will be additionally required. By delving into the novel applications of AI in healthcare administration, financial management, and synergies between these domains, this work can benefit a diverse audience across various sectors of the healthcare industry, technology, public administration, and academia. Limitations and future directions of research are provided.

Supply chain management professionals’ proficiency in big data analytics: Antecedents and impact on performance

While big data analytics (BDA) carries great potential for supply chain management (SCM), actual advanced applications of these approaches are still limited in practice. Within this context, we seek to identify what can foster an SCM professional’s proficient use of BDA, and define BDA proficiency as an individual’s ability to effectively use BDA. Grounding our research in the theoretical framework of technology acceptance, we consider perceived ease of use, perceived usefulness, attitude toward, and behavioral intention to use BDA as potential influential antecedents. Using primary survey data from 212 SCM professionals, we find that all but one of these hypothesized relationships are confirmed among the sample. This offers valuable insight on how to drive the intention of SCM professionals to use BDA, which serves as a foundation to become proficient in it. In addition, we hypothesize about the impact of BDA proficiency on generating business value in terms of both quality and cost performance, two important outcome variables in SCM research. While the impact on cost performance is confirmed, the impact on quality performance is non-significant, bringing greater clarity to the differentiated performance impacts of BDA proficiency. When further scrutinizing these links to performance under the moderating effect of environmental competitiveness, we not only find the impact of BDA proficiency on cost performance to be greater under higher levels of environmental competitiveness, but also that under such greater levels the link from BDA proficiency to quality performance becomes significant. Overall, our research provides insight not only into what can foster an SCM professional’s BDA proficiency, but also into its impact on performance under various levels of environmental competitiveness.

Creating business value with process mining

Information systems research has a long-standing interest in how organizations gain value through information technology. In this article, we investigate a business process intelligence (BPI) technology that is receiving increasing interest in research and practice: process mining. Process mining uses digital trace data to visualize and measure the performance of business processes in order to inform managerial actions. While process mining has received tremendous uptake in practice, it is unknown how organizations use it to generate business value. We present the results of a multiple case study with key stakeholders from eight internationally operating companies. We identify key features of process mining – data & connectivity, process visualization, and process analytics – and show how they translate into a set of affordances that enable value creation. Specifically, process mining affords (1) perceiving end-to-end process visualizations and performance indicators, (2) sense-making of process-related information, (3) data-driven decision making, and (4) implementing interventions. Value is realized, in turn, in the form of process efficiency, monetary gains, and non-monetary gains, such as customer satisfaction. Our findings have implications for the discourse on IT value creation as we show how process mining constitutes a new class of business intelligence & analytics (BI&A) technology, that enables behavioral visibility and allows organizations to make evidence-based decisions about their business processes.

Edge and Fog Computing Business Value Streams through IoT Solutions: A Literature Review for Strategic Implementation

Edge–fog computing and IoT have the ability to revolutionize businesses across all sectors and functions, from customer engagement to manufacturing, which is what makes them so fascinating and emerging. On the basis of research methodology by Webster and Watson (2020), 124 peer-reviewed articles were discussed. According to the literature, these technologies lead to reduced latency, costs, bandwidth, and disruption, but at the same time, they improved response time, compliance, security and greater autonomy. The results of this review revealed the open issues and topics which call for further research/examination in order for edge–fog computing to unveil new business value streams along with IoT capabilities for the organizations. Only by adopting and implementing precisely these revolutionary will new solutions organizations succeed in the digital transformation of the modern era. Despite the fact that they are cutting-edge solutions to business operations and knowledge creation, there are still practical implementation issues to be dealt with and a lack of experience in the strategic integration of the variable architectures, which hinder efforts to generate business value.

AI-Based Real-Time Site-Wide Optimization for Process Manufacturing

In this paper, we propose a site-wide lead advisor, which is an artificial intelligence–based prediction and set-point recommendation engine, by combining the use of machine learning with optimization techniques. It provides operational set-point recommendations to continuously improve site-wide operations for throughput measured in additional barrels of oil produced per day. A key contribution and differentiator is the utilization of sensor data to continuously learn the behavior of all the subsystems of an oil-producing plant and use this within an optimization framework to provide advisory control in near real time. This is novel in that it does not require a model of the plant to be provided as input. The predictive model is learned automatically and continuously from data. This work required the development of a new prediction-optimization modeling framework that optimizes throughput while staying in the vicinity of the historical process behavior and employing the model’s structure in designing algorithms to solve it. This solution has been deployed at Suncor Energy, an oil-sands company, since January 2019 and is estimated to generate business value in the order of tens of millions of dollars per year. The generalized approach of this framework lends it the ability to be applied to any processing or manufacturing plant.

An insight of Facebook usage by Pakistan-based organisations: an application of netnography approach

The power of social media to engage with people and generate business value for organisations is indisputable. Although, the existing literature draws some interesting findings on the use of Facebook by small and medium enterprises (SMEs), there is a dearth of literature on the validation of such findings using Facebook metadata. This research aims to examine the various aspects of Facebook campaigns for SMEs and their effectiveness in terms of engaging with the customers. The findings of this paper are scaffold into two parts. First, it performed qualitative data analysis to understand the types of posts uploaded by the SMEs. Second, it examined the Facebook metadata to understand the types of responses, their popularity and the correlation among several types of responses. It is anticipated that the findings of this paper would help SMEs to better understand their Facebook campaigns and to better manage their Facebook campaigns in the future.

The development of data analytics maturity assessment framework: DAMAF

AbstractToday, data analytics plays a vital role in attaining competitive advantage, generating business value, and driving revenue streams for organizations. Thus, the organizations pay significant attention to improve their data analytics maturity. Nevertheless, the existing literature is dramatically limited in proposing a comprehensive roadmap to assist organizations for this scope. Thus, this study focuses on developing data analytics maturity assessment framework (DAMAF) that evaluates the organizational data analytics maturity in a staged manner from maturity level 0: incomplete to maturity level 5: optimizing. The DAMAF comprises the nine different data analytics attributes to address the specific needs of each data analytics maturity level. Accordingly, it aims to support organizations in assessing their current data analytics maturity, determining organizational gaps in data analytics, and preparing an extensive roadmap and suggestions for data analytics maturity improvement. In this research, we employed the DAMAF in an organization as a case study to evaluate its applicability and usefulness. The results showed that DAMAF properly reveals the data analytics gaps and provides a structured roadmap for continuously advancing the data analytics maturity of an organization.

Design and Development of an AIoT Architecture for Introducing a Vessel ETA Cognitive Service in a Legacy Port Management Solution.

Current Internet of Things (IoT) stacks are frequently focused on handling an increasing volume of data that require a sophisticated interpretation through analytics to improve decision making and thus generate business value. In this paper, a cognitive IoT architecture based on FIWARE IoT principles is presented. The architecture incorporates a new cognitive component that enables the incorporation of intelligent services to the FIWARE framework, allowing to modernize IoT infrastructures with Artificial Intelligence (AI) technologies. This allows to extend the effective life of the legacy system, using existing assets and reducing costs. Using the architecture, a cognitive service capable of predicting with high accuracy the vessel port arrival is developed and integrated in a legacy sea traffic management solution. The cognitive service uses automatic identification system (AIS) and maritime oceanographic data to predict time of arrival of ships. The validation has been carried out using the port of Valencia. The results indicate that the incorporation of AI into the legacy system allows to predict the arrival time with higher accuracy, thus improving the efficiency of port operations. Moreover, the architecture is generic, allowing an easy integration of the cognitive services in other domains.

A Bilingual Comparison of Sentiment and Topics for a Product Event on Twitter

Social media enable companies to assess consumers’ opinions, complaints and needs. The systematic and data-driven analysis of social media to generate business value is summarized under the term Social Media Analytics which includes statistical, network-based and language-based approaches. We focus on textual data and investigate which conversation topics arise during the time of a new product introduction on Twitter and how the overall sentiment is during and after the event. The analysis via Natural Language Processing tools is conducted in two languages and four different countries, such that cultural differences in the tonality and customer needs can be identified for the product. Different methods of sentiment analysis and topic modeling are compared to identify the usability in social media and in the respective languages English and German. Furthermore, we illustrate the importance of preprocessing steps when applying these methods and identify relevant product insights.