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Creating a real-time data integration when developing an internet-of-things (IoT)-based warehouse is still faced with challenges. It involves a diverse knowledge of novel technology and skills. This study aims to identify the critical components of the real-time data integration processes in IoT-based warehousing. Then, design and apply a data integration framework, adopting the IoT concept to enable real-time data transfer and sharing.

The study used a pilot experiment to verify the data integration system configuration. Radio-frequency identification (RFID) technology was selected to support the integration process in this study, as it is one of the most recognized products of IoT.

The experimentations’ results proved that data integration plays a significant role in structuring a combination of assorted data on the IoT-based warehouse from various locations in a real-time manner. This study concluded that real-time data integration processes in IoT-based warehousing could be generated into three significant components: configuration, databasing and transmission.

While the framework in this research was carried out in one of the developing counties, this study’s findings could be used as a foundation for future research in a smart warehouse, IoT and related topics. The study provides guidelines for practitioners to design a low-cost IoT-based smart warehouse system to obtain more accurate and timely data to support the quick decision-making process.

The research at hand provides the groundwork for researchers to explore the proposed theoretical framework and develop it further to increase inventory management efficiency of warehouse operations. Besides, this study offers an economical alternate for an organization to implement the integration software reasonably.

The goal is to examine the relationship between business process improvement initiatives (BPII), information systems (ISs) integration, and customer focus.

The data for the study were collected at the divisional levels from a list of “Fortune 500” firms. A total of 1,100 questionnaires were mailed to divisional managers and 115 responses were used for the data analysis.

The results of this study support the hypotheses that BPII directly affects customer responsiveness and product/service innovation. In addition, data integration and communication networks flexibility are found to significantly affect customer responsiveness and product/service innovation. Communication networks connectivity did not have any significant effect on customer responsiveness and product/service innovation, respectively. Data integration, intrafirm networks connectivity and networks flexibility are found to significantly mediate the relationship between BPII and customer responsiveness and BPII and product/service development, respectively. Interfirm networks connectivity was not found to significantly mediate the relationship between BPII and customer responsiveness and BPII and product/service development, respectively.

The sample consists of divisions of Fortune 500 firms. Second, the study is cross‐sectional; therefore, it lacks the predictive power to find the long‐term effects of BPII and IS integration on customer focus. A longitudinal study can be a future research endeavor to validate the results of the study.

To business managers, the study clarifies the role of IS integration in customer focus.

Since the mid‐1980s, the subject of information systems (IS) integration and its role in business process improvement (BPI) has been of considerable interest to business managers and researchers. The present study examines the effects of IS integration on BPI. The moderating effects of industry type, information intensity of the industry, and time since the initiation of the existing program in the firm have also been analyzed. The data for the study were gathered through a survey of Fortune 500 US firms at divisional levels. The results of the study support the hypotheses that data integration and communication networks integration affect the dimensions of BPI. However, none of the moderators, except industry type, was found significantly affecting the relationships between networks integration and process improvement effect and customer focus.

This paper aims to explore how omni-channel data flows should be integrated by specifying what data, omni-channel agents and information and digital technologies (IDTs) should be considered and connected.

A multiple case study method is used with 17 British companies. The studies are supported by 68 interviews with the case companies and their consumers, 5 site visits, 4 focus group meetings and the companies’ archival data and documentations.

This paper provides novel frameworks for omni-channel data flow integration from consumer and business perspectives. The frameworks consist of omni-channel agents, their data transactions and their supporting IDTs. Relatedly, this paper formalizes the omni-channel data flow integration in the forms of horizontal, vertical and total integrations and explores their contributions to the adaptability of omni-channel, as a complex adaptive system (CAS). It also discusses that how inter-organizational governance mechanisms can support data flow integration and their relevant IDT implementations.

The breadth and depth of the required IDTs for omni-channel integration prove the necessity for omni-channel systems to move toward total integration. Therefore, supported by CAS and inter-organizational governance theories, this research indicates how data flow integration and IDT can transform the omni-channel through self-organization and autonomy capability enhancement.

This research’s recommended frameworks provide a robust platform to formalize data flow integration as the omni-channel's core driver. Accordingly, it moves the literature from a basic description of “what omni-channel is” and provides a novel and significant debate on what specific data should be shared at what levels between which agents of the omni-channel, and with what type of relationship governance mechanism, to assure omni-channel horizontal, vertical and total integrations.

This paper seeks to investigate the role of information systems integration in Irish small and medium‐sized enterprises operating in the manufacturing sector.

Research was conducted through a review of literature and subsequent primary research involving qualitative (semi‐structured interviews) and quantitative (questionnaires) research strategies.

The paper reveals the sophistication of internal IT infrastructure within Irish manufacturing SMEs and whether the IT systems are integrated with one another, and, if so, how well that integration is done. Moreover, the owner‐managers' and senior managers' attitude to IS integration issues is explored, including the reasons that prompt them to integrate IT systems within their businesses, their expectations of IS integration, the challenges they recognise when integrating the systems and their criteria regarding IS integration.

The research focuses on manufacturing SMEs operating in Ireland; other sectors are not investigated.

The paper helps the owner‐managers and senior managers to understand the issues of IS integration and points towards possible solutions to the problem of disparate IT systems.

The negative impact of disparate systems and the benefits of integrating them in an SMEs environment have not been thoroughly examined to date.

This paper aims to analyze the impact of information technology (IT) system implementation on the integration of data and information between sales and accounting departments, and how data integration affects relations with supplier and customers.

The change between three different reservation and distribution systems in an airline company was assessed over 20 years based on qualitative data collected while events unfolded and interviews that traced events retrospectively.

This study finds that data integration challenges affect the capacity to use revenue and sales data for control purposes and integrating with suppliers and customers. The systems either facilitated the ability to integrate sales and accounting data or enabled integration in wider supplier and customer networks. The implementation of different reservation and distribution systems resulted in a trade-off between integration within the firm and into wider customer and supplier networks.

Data were mainly obtained from the focal firm, Air Greenland. The protracted study period meant that the data were not as concentrated as they would have been had the analysis been performed over a shorter duration or had the focus been on one implementation process.

Extant research suggests that integration challenges when implementing IT systems are caused by differences in information needs between groups with different logics. The authors illustrate how data integration is also a crucial challenge when implementing IT systems.

Smart city services are supported by information and communication technologies (ICT) referred to as digital technologies which increasingly promise huge opportunities for growth but are faced with system alignment and data integration issues when providing digital services. Therefore, this study aims to use enterprise architecture (EA) in digital transformation of cities by developing an architecture to address system alignment and data integration in digital transformation of cities.

Qualitative method is applied to evaluate the presented architecture based on electric-mobility (e-mobility) scenario, and data was collected using case study via interviews from a municipality in Norway to validate the applicability of EA for digital transformation of city services.

Findings from the interviews were represented in ArchiMate language to model the digital transformation of e-mobility in smart cities. Findings suggest that the architecture serves as a guide to recommend urban administrators of the potential of EA and digital transformation in addressing system alignment and data integration issues in smart cities.

Data used in this study is from a single case, hence there is a need to evaluate the application of EA for digital transformation of city services with data collected from multi-cases.

This study adopts enterprise architecture approach to support city transformation as it has been widely applied by institutions to align business and ICT components.

This study provides implication on how municipalities can use EA and digital transformations towards a sustainable smart city.

An architecture is presented that can be used as a guide to help urban developers and designers in deploying sustainable transport policies for smart cities. Additionally, EA is used to foster digitalization towards achieving system alignment and data integration in cities to support urban environment as they digitally transform services provided to citizens.

The purpose of this paper is to propose an integrated approach to process integration, automation, and optimization through enhanced business process models.

The approach is based on a framework of process integration for functional applications, automation for business workflows, and additional functionalities for process optimization. The proposed approach is illustrated using enhanced process models over business integration, automation, and optimization with data elements, structures, and organizational elements. The standard sales order process cycle, quotation approval process, and production order cycle are chosen for illustrating process integration, automation, and optimization, respectively.

The proposed approach combines applications and workflows using integrated process/data models and forms a foundation for business process optimization. It is shown that the integrated approach can improve existing business processes in enterprise resource planning (ERP), beyond business process re‐engineering (BPR) principles, once enhanced business process models are implemented. This approach eliminates need for a hierarchical representation of business processes and highlights the flexibility and visibility of business process implementation in ERP system environment.

Although process integration, automation, and optimization are illustrated using selected business process examples, it requires generalization of these enhancements over entire business blueprint of ERP system. Thus, one key limitation of this research is that it is not generalized for the entire business blueprint of ERP. This also requires changes to data structures beyond current relational data in many ERP systems.

This research provides an integrated approach to business process modeling beyond traditional functional and workflow applications by eliminating hierarchical nature of process and data elements.

The application landscapes of major companies all have their own complex structure. Data have to be exchanged between or distributed to the various applications. Systemizing different data integration patterns on a conceptual level can help to avoid uncontrolled redundancy and support the design process of data integration solutions. Each pattern provides a solution for certain data integration requirements and makes the design process more effective by reusing approved solutions. Proposes identifying these patterns.

After a broad literature review data were obtained from interviews and documentary sources. Ten semi‐structured interviews were conducted within four different companies operating in the financial service industry. EAI‐ and IT‐architects as well as project managers and CTOs were involved in these interviews.

Five different data integration patterns were identified. Solutions for upcoming data integration requirements can be designed using these patterns. Advantages and disadvantages as well as typical usage scenarios are discussed for each identified data integration pattern.

In order to identify data dependencies, to detect redundancies and to conduct further investigations, a consistent methodology for the description of application landscapes has to be developed. The presented design patterns are one part of this methodology only. The approach in this paper only considers data integration while in reality there are also other integration requirements like functional or process‐oriented integration.

The identified design patterns help practitioners (e.g. IT‐architects) to design solutions for data integration requirements. They can map the conceptual patterns to company specific technologies or products to realize the solution physically.

The design patterns are indifferent from any technology or products which ensure a broad application. Business requirements (e.g. requirement for autonomous processing) are considered first when designing a data integration solution.

Upon the realization of the need for guideline in cross-organizational data integration, in an exploratory manner, this study developed a public data governance framework, specifically, the governance for integrated public data (GIPD) framework and identified the influential factors of its successful implementation. This framework was then subjected to an analysis of a real data integration case in the South Korean public sector to test its efficacy.

To develop the GIPD framework, the authors conducted an extensive meta study, focus group interviews and the analytic hierarchy process involving field experts. Further, the authors performed topic modeling on documents from Korean research and development data integration projects, and compared the extracted factors to those of the GIPD to illustrate the latter's usefulness in a real case.

Legislation, policy goals and strategies, operation organization, decision-making council, financial support size and objective, system development and operation, data integration, data generation, system/data standardization and master data management were derived as the 10 important factors in implementing the GIPD framework. The illustrative case of Korea revealed that decision-making council, financial support size and objective, legislation, data generation and data integration were insufficient.

Although this study reveals important findings, it has a few limitations. First, the potential factors for data governance might vary depending on the attribute of the “interviewee” (such as their career or experience period) and the goal and area of GIPD framework building. Second, the inherent limitation of topic modeling in determining topics from groups of extracted keywords means that topics may be interpreted in various ways, depending on the perspective of the expert.

This study is highly significant in that it provides a starting point for discussions on the issue of data integration among public institutions. Therefore, although this study examined public data governance based on R&D data, it will contribute to providing a sufficient guideline for any type of inter-institutional data governance framework, what to discuss and how to discuss between institutions.

The findings are expected to provide a roadmap to formulate practical guidelines on inter-institutional data cooperation and a diagnostic matrix to improve the existing data governance system, especially in the public sector, from the existing practice of empirical analysis using a mixed methodology approach.