Factors influencing effective use of big data: A research framework

Flood susceptibility mapping of Cheongju, South Korea based on the integration of environmental factors using various machine learning approaches

Floods are natural occurrences that pose serious risks to human life and the environment, including significant property and infrastructure damage and subsequent socioeconomic challenges. Recent floods in Cheongju County, South Korea have been linked to river overflow. In this study, we created flood susceptibility maps of Cheongju, South Korea using machine learning techniques including support vector regression (SVR), boosted tree (BOOST), and long short-term memory (LSTM) algorithms, based on environmental factors. Potentially influential variables were selected based on flood data gathered through field surveys; these included the slope, aspect, length-slope factor, wind exposition index, terrain wetness index, plan curvature, normalized difference water index, geology, soil drainage, soil depth, soil texture, land use type, and forest density. To improve the robustness of the flood susceptibility model, the most influential factors were identified using the frequency ratio method. Implementing machine learning techniques like SVR and BOOST produced encouraging outcomes, achieving the area under the curve (AUC) of 83.16% and 86.70% for training, and 81.65% and 86.43% for testing, respectively. While, the LSTM algorithm showed superior flood susceptibility mapping performance, with an AUC value of 87.01% for training and 86.91% for testing, demonstrating its robust performance and reliability in accurately assessing flood susceptibility. The results of this study enhance our understanding of flood susceptibility in South Korea and demonstrate the potential of the proposed approach for informing and guiding crucial regional policy decisions, contributing to a more resilient and prepared future.

Enhancing ERP Responsiveness Through Big Data Technologies: An Empirical Investigation

Organizations are integrating big data technologies with Enterprise Resource Planning (ERP) systems with an aim to enhance ERP responsiveness (i.e., the ability of the ERP systems to react towards the large volumes of data). Yet, organizations are struggling to manage the integration between the ERP systems and big data technologies, leading to lack of ERP responsiveness. For example, it is difficult to manage large volumes of data collected through big data technologies and to identify and transform the collected data by filtering, aggregating and inferencing through the ERP systems. Building on this motivation, this research examined the factors leading to ERP responsiveness with a focus on big data technologies. The conceptual model which was developed through a systematic literature review was tested using Structural equation modelling (SEM) performed on the survey data collected from 110 industry experts. Our results suggested 12 factors (e.g., big data management and data contextualization) and their relationships which impact on ERP responsiveness. An understanding of the factors which impact on ERP responsiveness contributes to the literature on ERP and big data management as well as offers significant practical implications for ERP and big data management practice.

Characterizing stewardship and stakeholder inclusion in data analytics efforts: the collaborative approach of Kansas City, Missouri

Purpose

Local governments face increasingly complex challenges related to their internal operations as well as the provision of public services. However, research on how they embrace evidence-based approaches such as data analytics practices, which could help them face some of those challenges, is still scarce. This study aims to contribute to existing knowledge by examining the data analytics practices in Kansas City, Missouri (KCMO), a city that has become prominent for engaging in data analytics use through the Bloomberg’s What Works Cities (WWC) initiative with the purpose of improving efficiency and enhancing response to local constituents.

Design/methodology/approach

This research conducted semistructured interviews with public servants who had data analytics experience at KCMO. Analysis looked for common and emerging patterns across transcripts. A conceptual framework based on related studies is built and used as the theoretical basis to assess the evidence observed in the case.

Findings

Findings suggest that data analytics practices are sponsored by organizational leadership, but fostered by data stewards who engage other stakeholders and incorporate data resources in their analytical initiatives as they tackle important questions. Those stewards collaborate to nurture inclusive networks that leverage knowledge from previous experiences to orient current analytical endeavors.

Research limitations/implications

This study explores the experience of a single city, so it does not account for successes and failures of similar local governments that were also part of Bloomberg's WWC. Furthermore, the fact that selected interviewees were involved in data analytics at least to some extent increases the likelihood that their experience with data analytics is relatively more positive than the experience of other local government employees.

Practical implications

Results suggest that data analytics benefits from leadership support and steering initiatives such as WWC, but also from leveraging stakeholder knowledge through collaborative networks to have access to data and organizational resources. The interplay of data analytics sponsored activities and organizational knowledge could be used as means of assessing local governments’ existing data analytics capability.

Originality/value

This study suggests that data analytics practices in local governments that are implementing a smart city agenda are knowledge-driven and developed incrementally through inclusive networks that leverage stakeholder knowledge and data resources. The incrementality identified suggests that data analytics initiatives should not be considered a “blank slate” practice, but an endeavor driven and sustained by data stewards who leverage stakeholder knowledge and data resources through collaborative networks.

How do firms create business value and dynamic capabilities by leveraging big data analytics management capability?

Despite researchers having averred that big data analytics (BDA) transforms firms' ways of doing business, knowledge about operationalizing these technologies in organizations to achieve strategic objectives is lacking. Moreover, organizations' great appetite for big data and limited empirical proof of whether BDA impacts organizations' transformational capacity poses a need for further empirical investigation. Therefore, this study explores the association between big data analytics management capabilities (BDAMC) and innovation performance via dynamic capabilities (DC), by applying the PLS-SEM technique to analyzing the feedback of 149 firms. Consequently, we ground our arguments on dynamic capability and social capital theory rather than a resource-based view that does not provide suitable explanations for the deployment of resources to adapt to change. Accordingly, we advance this research stream by finding that BDAMC significantly enhances innovation performance through DC. We also extend the literature by disclosing how BDAMC strengthens DC via strategic alignment and social capital.

A scholarly network of AI research with an information science focus: Global North and Global South perspectives

This paper primarily aims to provide a citation-based method for exploring the scholarly network of artificial intelligence (AI)-related research in the information science (IS) domain, especially from Global North (GN) and Global South (GS) perspectives. Three research objectives were addressed, namely (1) the publication patterns in the field, (2) the most influential articles and researched keywords in the field, and (3) the visualization of the scholarly network between GN and GS researchers between the years 2010 and 2020. On the basis of the PRISMA statement, longitudinal research data were retrieved from the Web of Science and analyzed. Thirty-two AI-related keywords were used to retrieve relevant quality articles. Finally, 149 articles accompanying the follow-up 8838 citing articles were identified as eligible sources. A co-citation network analysis was adopted to scientifically visualize the intellectual structure of AI research in GN and GS networks. The results revealed that the United States, Australia, and the United Kingdom are the most productive GN countries; by contrast, China and India are the most productive GS countries. Next, the 10 most frequently co-cited AI research articles in the IS domain were identified. Third, the scholarly networks of AI research in the GN and GS areas were visualized. Between 2010 and 2015, GN researchers in the IS domain focused on applied research involving intelligent systems (e.g., decision support systems); between 2016 and 2020, GS researchers focused on big data applications (e.g., geospatial big data research). Both GN and GS researchers focused on technology adoption research (e.g., AI-related products and services) throughout the investigated period. Overall, this paper reveals the intellectual structure of the scholarly network on AI research and several applications in the IS literature. The findings provide research-based evidence for expanding global AI research.

Sustainable Development: Smart Co-Operative Management Framework

A smart co-operative refers to a co-operative that aims to apply ICT to provide better services and increase management efficiency to meet organizations’ goals through the combinations of ICT technology and business. In this paper, we propose the sustainable development smart co-operative framework, which specifically applies to all types of co-operatives which use information technology in their organization, enabling transformation to improve their services, management, and governance. In addition, we discuss ICT channel creation for improving knowledge, awareness, democracy, and the participation of members, a process in which IT contributes to the accessibility of members and communication between the co-operative, members, and stakeholders. The element design of this proposed framework has considered three key principles, which are (1) smart members, (2) the smart economy, and (3) smart governance. A smart co-operative is a term used to extend the concept of a smart city into co-operative organization to promote a sustainable development approach in the co-operative sector. Therefore, the smart co-operative combines ICT, smart concepts, co-operative business aspects, business models, and innovation. The findings suggest that the smart and sustainable development co-operative framework is suitable for co-operatives, providing a comprehensive framework for value creation through the smart co-operative concept.

Towards an understanding of actual business intelligence technology use: an individual user perspective

Purpose

Although much is understood about Business Intelligence (BI) technology adoption, less is known about the complementary organisational resources that drive the actual use of BI systems and the impacts of BI systems at an individual employee level. This study aims to develop and test a model of the impact of key complementary organisational resources on employees' actual BI systems’ use behaviours and their decision-making performance.

Design/methodology/approach

To test the research model, a cross-sectional survey of 437 North American employees, who described themselves as using a BI system to make decisions, was conducted. The partial least square (PLS), a structural equational modelling (SEM) technique, was employed to analyse the survey data.

Findings

The survey findings attest to the influence of key complementary organisational resources (i.e. data-based culture (DBC), quality of data in source systems and decision-making autonomy) on employees' actual BI use (comprising BI system dependence and BI system infusion) and on their decision-making performance. Specifically, a DBC and the quality of data in source systems are found to significantly enhance BI system dependence and BI system infusion. Decision-making autonomy, DBC, BI system dependence and BI system infusion are significant contributors to achieving decision-making performance.

Originality/value

This study proposes a theoretical model of actual BI systems’ use from an individual user perspective that increases our understanding of both the complexity of BI usage and the complementary organisational resources that drive both actual BI systems’ use and the impacts of BI systems.

Digital supply chain management in the COVID-19 crisis: An asset orchestration perspective

Although many firms are actively deploying various digital technology (DT) assets across their supply chains to mitigate the negative impact of the COVID-19 pandemic on operations, whether these DT assets are truly helpful remains unclear. To disentangle this puzzle, we investigate whether firms that have higher levels of DT asset deployment achieve better supply chain performance in the COVID-19 crisis than firms with lower levels. From an asset orchestration perspective, we focus on two dimensions of DT asset deployment: breadth and depth, which reflect the scope and scale of DT assets, respectively. The empirical results from 175 Chinese firms that have deployed DT assets to varying degrees reveal that both the breadth and the depth of DT asset deployment show positive relationships with supply chain visibility. In contrast, the depth but not the breadth of DT asset deployment poses a positive relationship with supply chain agility. Most importantly, high levels of supply chain visibility and supply chain agility were prerequisites for excellent supply chain performance in the COVID-19 crisis. We contribute to the digital supply chain management literature by uncovering the mechanism through which DT asset deployment generates impacts on supply chain performance from an asset orchestration perspective. Our study also assists firms in improving their digital transformation strategies to combat the COVID-19 pandemic.

Factors Influencing the Adoption of Big Data Analytics in the Digital Transformation Era: Case Study of Jordanian SMEs

Big data (BD) analytics has been increasingly gaining attraction in both practice and theory in light of its opportunities, barriers and expected benefits. In particular, emerging economics view big data analytics as having great importance despite the fact that it has been in a constant struggle with the barriers that prevent its adoption. Thus, this study primarily attempted to determine the drivers of big data analytics in the context of a developing economy, Jordan. The study examined the influence of technological, organizational and environmental factors on big data adoption in the Jordanian SMEs context, using PLS-SEM for the analysis. The empirical results revealed that the relative advantage, complexity, security, top management support, organizational readiness and government support influence the adoption of BD, whilst pressure of competition and compatibility appeared to be of insignificant influence. The findings are expected to contribute to enterprise management and strategic use of data analytics in the present dynamic market environment, for both researcher and practitioner circles concerned with the adoption of big data in developing countries.

A Model for Decision-Makers’ Adoption of Big Data in the Education Sector

Big Data Adoption (BDA) has already gained tremendous attention from executives in various fields. However, it is still not well explored in the education sector, where a large amount of academic data is being produced. Therefore, integrating Technology Organization Environment (TOE) and Diffusion of Innovation (DOI), this study aims to develop a theoretical model to identify the factors that influence BDA in the higher education sector. To do so, significant technology-, organization-, and environment-related factors have been extracted from previous BDA studies. Meanwhile, the moderating effects of the university size and the university age are added into the developed model. A sample of 195 data was collected from the managerial side of virtual university (VU) campuses in Pakistan using an online survey questionnaire. Structural equation modeling (SEM) was used to test the research model and developed hypotheses. The results showed that relative advantage, complexity, compatibility, top management support, financial resources, human expertise and skills, competitive pressure, security and privacy, and government policies are significant determinants of BDA. However, the results did not support the influence of IT infrastructure on BDA. Based on the findings, this study provides guidelines for the successful adoption of big data in higher education sector. This study can serve as a piece of help to the ministry of education, administrators, and big data service providers for the smooth adoption of big data.

Barriers to the digital transformation of infrastructure sectors

Digital technologies can be important to policy-makers and public servants, as these technologies can increase infrastructure performance and reduce environmental impacts. For example, utilizing data from sensors in sewer systems can improve their management, which in turn may result in better surface water quality. Whether such big data from sensors is utilized is, however, not only a technical issue, but also depends on different types of social and institutional conditions. Our article identifies individual, organizational, and institutional barriers at the level of sub-states that hinder the evaluation of data from sewer systems. We employ fuzzy-set Qualitative Comparative Analysis (fsQCA) to compare 23 Swiss sub-states and find that two barriers at different levels can each hinder data evaluation on their own. More specifically, either a lack of vision at the individual level or a lack of resources at the organizational level hinder the evaluation of data. Findings suggest that taking into account different levels is crucial for understanding digital transformation in public organizations.

Construction of Community Life Service in the Sharing Economy Based on Deep Neural Network

Currently, the development of sharing economy and interconnection also has a profound impact on community life services. This study is based on the deep neural network theory, combined with the evolution mechanism of the commercial network of the community life service industry, link prediction theory, and the latest deep neural network algorithm, referring to the evolution model of merger and stripping, and the network structure is optimized on this basis. Through simulation experiments and result analysis, the model is used to deeply study the evolution trend and dynamics of the community life service business network from the perspective of quantitative analysis. Then the business network structure is optimized and development is promoted at the same time. At the same time, it can also upgrade those old scattered industries and provide theoretical and decision-making guidance for the future transformation and upgrading of the innovative community life service industry.

The Impacts of the Fourth Industrial Revolution on Smart and Sustainable Cities

This article aims to analyze the impacts of the Fourth Industrial Revolution on the implementation of smart sustainable cities. For this purpose, a data mining process was conducted to analyze the terms that had a higher incidence in the literature in order to classify them by relevance and identify their interdependencies in the concepts of sustainable cities and smart cities. As a result, we highlight that the Fourth Industrial Revolution will have implications on several factors that are deeply connected to the success of cities in becoming sustainable: job creation, industries, innovation, environmental preservation, community involvement, and accessibility. In this context, policymakers will have opportunities and challenges that must be faced. Big data, the IoT, augmented reality, and simulations can have positive and negative externalities. Positive externalities include new information that could be mined, analyzed, and used for identifying previously unseen problems, the provision of new industrial innovations that can make economies thrive, helping promote inclusion for disabled people, as well as helping society to foresee problems and hence adapt to them in a timely manner.