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Lean manufacturing has been pivotal in emphasizing the reduction of cycle times, minimizing manufacturing costs and diminishing inventories. This research endeavors to formulate a lean data management paradigm, through the design and execution of a strategic edge-cloud data governance approach. This study aims to discern anomalous or unforeseen patterns within data sets, enabling an efficacious examination of product shortcomings within manufacturing processes, while concurrently minimizing the redundancy associated with the storage, access and processing of nonvalue-adding data.

Adopting a lean data management framework within both edge and cloud computing contexts, this study ensures the preservation of significant time series sequences, while ascertaining the optimal quantity of normal time series data to retain. The efficacy of detected anomalous patterns, both at the edge and in the cloud, is assessed. A comparative analysis between traditional data management practices and the strategic edge-cloud data governance approach facilitates an exploration into the equilibrium between anomaly detection and space conservation in cloud environments for aggregated data analysis.

Evaluation of the proposed framework through a real-world inspection case study revealed its capability to navigate alternative strategies for harmonizing anomaly detection with data storage efficiency in cloud-based analysis. Contrary to the conventional belief that retaining comprehensive data in the cloud maximizes anomaly detection rates, our findings suggest that a strategic edge-cloud data governance model, which retains a specific subset of normal data, can achieve comparable or superior accuracy with less normal data relative to traditional methods. This approach further demonstrates enhanced space efficiency and mitigates various forms of waste, including temporal delays, storage of noncontributory normal data, costs associated with the analysis of such data and excess data transmission.

By treating inspected normal data as nonvalue-added, this study probes the intricacies of maintaining an optimal balance of such data in light of anomaly detection performance from aggregated data sets. Our proposed methodology augments existing research by integrating a strategic edge-cloud data governance model within a lean data analytical framework, thereby ensuring the retention of adequate data for effective anomaly detection.

Agriculture is one sector where the Internet of things (IoT) is expected to make a major impact. Yet, its adoption in the sector falls behind expectations. The purpose of this paper is to present the state-of-the-art of IoT in agriculture and investigate its slow adoption in the sector.

The authors have undertaken a systematic review and a synthesis of 1355 relevant publications over the last decade.

This literature review reveals that the “big three” barriers for the overall sector are cost, skills and standardization. The lack of connectivity and data governance are two key reasons why most of the proposed IoT solutions are standalone systems of limited scope, while the majority of commercial IoT efforts focus on practices in the protected indoor environment. Lastly, the analysis of past research along the five layers of the IoT system architecture reveals limited attention to barriers and solutions at the business layer, which represents a research opportunity for information systems scholars.

It is possible that some of relevant publications were missed in the literature search, although the search queries were kept as broad as possible to avoid the exclusion of any relevant work. Any publication written in any other language other than English was excluded from the review. Given the geographical distribution of the reviewed English publications (see section 4.1), it is highly likely that important works written by Chinese and European scholars in their native language were overlooked.

This study provides practical insights into the technical and organisational challenges on the ground. It is the hope that this literature review lays the groundwork for IS researchers who are well positioned to investigate technology adoption challenges in the relatively understudied agriculture sector.

To the best of the authors’ knowledge, this is the first comprehensive review of adoption barriers and solutions across all five layers of the IoT system architecture.

This article aims to extend the Unified Theory of Acceptance and Use of Technology (UTAUT) model to understand the factors affecting the usage behavior of Blockchain from accountants' and auditors’ perspectives and its impact on their performance.

A quantitative research approach employing a web-based questionnaire was applied, and the empirical data were gathered from 329 potential and current users of Blockchain in the accounting and auditing profession in Jordan. The analytical model was based on structural equation modeling (SEM) using AMOS 25.0.

The experimental findings of the structural path confirmed that performance expectancy (PE), social influence (SI), Blockchain transparency (BT) and Blockchain efficiency (BE) were significantly affecting individuals’ behavioral intention (BI) toward the use of Blockchain-based systems and helped to explain (0.67) of its variance. Also, BE has a positive significant impact on PE. Whereas, in contrast to what is anticipated, the influence of effort expectancy (EE) on BI was not supported. Additionally, users’ intentions were found to affect the actual usage (AU) behavior and helped to explain (0.69) of its variance. The outcome variables proposed in this study: knowledge acquisition (KACQ) and user satisfaction (USAT) were significantly influenced by the AU of Blockchain technology.

This study outlines practical implications for government, policymakers, business leaders and Blockchain service providers aiming to exploit the advantages of Blockchain technology (BCT) in the accounting and auditing context.

To the best of the authors’ knowledge, this article is one of the few studies that offer an evidence-based perspective to the discussions on the effect of disruptive and automated information and communication technologies (ICTs), on the accounting and auditing profession. It applies an innovative approach to analysis through the integration of UTAUT, contextual factors: BT and BE, besides two outcome factors: KACQ and USAT within its theoretical model. This study extends and complements the academic literature on information technology/information systems acceptance and use by providing novel insights into accountants' and auditors’ views.

This work tries to detect the factors that can impact service innovation in the retail sector according to a service ecosystem (SES) perspective. This paper aims to understand whether it is possible to study innovation focusing on the impact of technology on resource integration practices in SESs and to rank different patterns of innovation by evaluating their effects in terms of value co-creation.

To show up the perception of actors, a case study has been carried out through semi-structured interviews. The aggregates of practices and the service innovation archetypes, drawn from the theoretical background, have been used as categories of analysis.

Service innovation is reconceptualised as the result of the application of new technology to resource integration practices in the retail SES, and it is possible to rank its patterns and outcomes by deepening its effects on the emergence of value co-creation phenomena. Shared intentions have been identified as drivers of service innovation, but greater transparency in systems used to embolden a higher willingness to use could be necessary.

Service innovation has been studied by focusing on value co-creation; for this reason, the willingness to use technology emerged as a determinant of service innovation. This result implies the need for a multilevel reinterpretation of contemporary SES, both regarding the technical features of digital solutions and their adherence to users' skills and the effects of willingness or unwillingness to use on value co-creation.

Road traffic emissions are generally believed to contribute immensely to air pollution, but the effect of road traffic data sets on air quality (AQ) predictions has not been fully investigated. This paper aims to investigate the effects traffic data set have on the performance of machine learning (ML) predictive models in AQ prediction.

To achieve this, the authors have set up an experiment with the control data set having only the AQ data set and meteorological (Met) data set, while the experimental data set is made up of the AQ data set, Met data set and traffic data set. Several ML models (such as extra trees regressor, eXtreme gradient boosting regressor, random forest regressor, K-neighbors regressor and two others) were trained, tested and compared on these individual combinations of data sets to predict the volume of PM_2.5, PM₁₀, NO₂ and O₃ in the atmosphere at various times of the day.

The result obtained showed that various ML algorithms react differently to the traffic data set despite generally contributing to the performance improvement of all the ML algorithms considered in this study by at least 20% and an error reduction of at least 18.97%.

This research is limited in terms of the study area, and the result cannot be generalized outside of the UK as some of the inherent conditions may not be similar elsewhere. Additionally, only the ML algorithms commonly used in literature are considered in this research, therefore, leaving out a few other ML algorithms.

This study reinforces the belief that the traffic data set has a significant effect on improving the performance of air pollution ML prediction models. Hence, there is an indication that ML algorithms behave differently when trained with a form of traffic data set in the development of an AQ prediction model. This implies that developers and researchers in AQ prediction need to identify the ML algorithms that behave in their best interest before implementation.

The result of this study will enable researchers to focus more on algorithms of benefit when using traffic data sets in AQ prediction.

This paper aims to propose a new method to derive custom dynamic cyber risk metrics based on the well-known Goal, Question, Metric (GQM) approach. A framework that complements it and makes it much easier to use has been proposed too. Both, the method and the framework, have been validated within two challenging application domains: continuous risk assessment within a smart farm and risk-based adaptive security to reconfigure a Web application firewall.

The authors have identified a problem and provided motivation. They have developed their theory and engineered a new method and a framework to complement it. They have demonstrated the proposed method and framework work, validating them in two real use cases.

The GQM method, often applied within the software quality field, is a good basis for proposing a method to define new tailored cyber risk metrics that meet the requirements of current application domains. A comprehensive framework that formalises possible goals and questions translated to potential measurements can greatly facilitate the use of this method.

The proposed method enables the application of the GQM approach to cyber risk measurement. The proposed framework allows new cyber risk metrics to be inferred by choosing between suggested goals and questions and measuring the relevant elements of probability and impact. The authors’ approach demonstrates to be generic and flexible enough to allow very different organisations with heterogeneous requirements to derive tailored metrics useful for their particular risk management processes.

Organizational innovation (OI) is important for multinational enterprises to adapt to changes in their broader technological and market environments. Despite its power to transform organizations, OI has remained at the periphery of international business (IB) scholarship. The purpose of this paper is that IB is particularly equipped to further the understanding of OI. IB studies place significant value on “context” and how the context in which the firm operates can enable or hinder the evolution of internal routines and practices, leading (or not) to OI.

The authors identify the key challenges which have contributed to the seemingly less important role of OI in IB, notable among them being the ambiguity of concepts associated with OI across different research fields. The authors advance the research agenda by offering a comprehensive definition of OI. The authors then put forward an integrative framework where the authors discuss the importance, and contribution, of IB to OI and vice versa.

The literature is characterized by terminological and empirical ambiguity. Some management scholars have coined the term “management innovation” with a clear element of invention and state-of-the-art attached to it. Others have referred to “organizational innovation,” when exploring incremental and targeted changes to extant team- and firm-level practices. In turn, IB scholars developed their own terminology, often (implicitly) referring to technological innovations as “asset-type firm-specific advantages” (FSAs) and associating OI with “transaction-type” FSAs.

The authors offer a new definition for OI – to address the challenges associated with terminological ambiguity. The authors put forward an integrative framework of OI in IB. The proposed framework of OI emphasizes the wider organizational context in which OI takes place, i.e. firm heterogeneity; and the broader external (IB) context of OI.

This project aims to optimise a secondary agricultural company’s reporting and data lifecycle by providing self-help business intelligence at an optimal price point for all business users.

A design for Lean Six Sigma approach utilising the define, measure analyse, design and verify methodology was utilised to design a new reporting and data product lifecycle.

The study found that this approach allowed a very structured delivery of a complex program. The various tools used assisted greatly in delivering results while balancing the needs of the team.

This study demonstrates how improving data analysis and enhanced intelligence reporting in agribusinesses enable better decision making and thus improves efficiencies so that the agribusiness can leverage the learnings.

Improving data analysis increases efficiency and reduces agrifood food wastage thus improving sustainability and environmental impacts.

This paper proposes creating a standardised approach to deploying Six Sigma methodology to correct both the data provisioning lifecycle and the subsequent business intelligence reporting lifecycle. It is the first study to look at process optimisation across the agricultural industry’s entire data and business intelligence lifecycle.

The purpose of this study is to conduct a systematic literature review of the adoption of immersive marketing technology (IMT) in terms of strategic planning of its adoption, resource requirements and its implications and challenges.

This study categorizes and contextualizes qualitative approaches to evaluate the literature, with Scopus databases serving as the primary source of 90 selected articles in the areas of information technology, business and marketing strands. Theme analysis was carried out using thematic techniques and grounded approach principles to facilitate thematic coding and generate theme analysis.

The analysis was supported by the three concepts of business flexibility, agility and adaptability, which were drawn as a strategy for IMT adoption. The findings presented three main themes: proactive flexibility, responsive agility and reactive adaptability that enable business owner–managers to craft a strategy for IMT adoption.

The novel contribution of this study is the inclusion of key implications related to IMT as a starting point of the next level of innovative marketing for all academics, practitioners and business owner–managers.