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The purpose of this article is to identify how the organisations are able to improve their business value through acquisition of business analytics capabilities and by improving their performance.

With the help of literature survey, along with standard resource-based view framework, a conceptual model has been developed. These have been statistically tested by collecting the data using the survey questionnaire from 306 selected respondents from various service sector- and product-based organisations in India. To analyse the data we have used partial least square–based structural equation modelling.

The study highlights that by the help of data acquisition and tool acquisition as two vital components the acquisition of business analytics capabilities could improve the business value of the organisation by strengthening its organisational performance. The findings of this research also indicated that acquisition of business analytics capabilities has a significant influence on an organisation's business process performance and business decision, which in turn significantly influences organisational performance. And organisational performance eventually positively influences its business value. The model was found to provide an explanative power of 71%.

The proposed research model can provide effective recommendations to the management of the organisations to realise the importance of acquisition of effective business analytics capabilities to eventually improve the business value of the organisation.

No specific studies, as yet, have analysed the effects of acquisition of business analytics capabilities for improving organisational performance mediated through business process performance and business decision. Therefore, this research has explored the distinctive effort to empirically validate this understanding.

While existing literature extensively explores manufacturing firms expanding into services, little is known about the modes of servitisation, the means by which they carry it out. This paper concentrates on acquisitions as a mode of servitisation. Post-acquisition integration is when the potential of an acquisition is realised. The paper therefore aims to categorise types of integrations following the acquisition of servitised firms and discusses their consequences for servitisation.

The empirical part of the paper is based on two case studies, each involving the acquisition of servitised firms. Both acquirers changed their integration approach over time.

The paper conceptualises three types of integrations: rhetorical, insulated and transformative integrations, indicating whether and how the acquirer becomes servitised following the integration. These highlight the analysis of integration based on business models and customer orientation in relation to servitisation.

This paper contributes to research on servitisation by emphasising acquisitions as a mode of servitisation and conceptualising three integration types related to business models and customer orientations. Furthermore, the paper highlights how an acquirer's servitisation leads to new offerings targeting new customers, as opposed to strengthening existing relationships.

The Overall Equipment Effectiveness (OEE) is considered a standard for measuring equipment productivity in terms of efficiency. Still, Artificial Intelligence solutions are rarely used for analyzing OEE results and identifying corrective actions. Therefore, the approach proposed in this paper aims to provide a new rule-based Machine Learning (ML) framework for OEE enhancement and the selection of improvement actions.

Association Rules (ARs) are used as a rule-based ML method for extracting knowledge from huge data. First, the dominant loss class is identified and traditional methodologies are used with ARs for anomaly classification and prioritization. Once selected priority anomalies, a detailed analysis is conducted to investigate their influence on the OEE loss factors using ARs and Network Analysis (NA). Then, a Deming Cycle is used as a roadmap for applying the proposed methodology, testing and implementing proactive actions by monitoring the OEE variation.

The method proposed in this work has also been tested in an automotive company for framework validation and impact measuring. In particular, results highlighted that the rule-based ML methodology for OEE improvement addressed seven anomalies within a year through appropriate proactive actions: on average, each action has ensured an OEE gain of 5.4%.

The originality is related to the dual application of association rules in two different ways for extracting knowledge from the overall OEE. In particular, the co-occurrences of priority anomalies and their impact on asset Availability, Performance and Quality are investigated.

This paper aims to give an oversight of what is being done by researchers in GIS and remote sensing (field) to explore minerals. The main objective of this review is to explore how GIS and remote sensing have been beneficial in identifying mineral deposits for easier and cost-effective mining.

The approach of this research used Web of Science to generate a database of published articles on the application of GIS and remote sensing techniques for mineral exploration. The literature was further digested, noting the main findings, adopted method, illustration and research scales.

When applied alone, each technique seems effective, but it is important to know that combining different methods is more effective in identifying ore deposits.

This paper also examined and provided possible solutions to both current and future perspective issues relating to the application of GIS and remote sensing to mineral exploration. The authors believe that the conclusions and recommendations drawn from case studies and literature review will be of great importance to geoscientists and policymakers.

Building permitting is mostly a manual, labor intensive and time-consuming process. Initiatives for streamlining the process are not always helpful since they often fail to address the core problems within the process. A framework for modeling the permitting process can be useful to identify bottlenecks, core challenges and best practices. Hence, the authors aim to demonstrate and validate a previously suggested workflow for permit process modeling using the permitting process in Israel as a test case.

The authors implement qualitative expert interviews for data acquisition. The collected data are then processed for a qualitative data analysis. The results of the analysis are then validated using a focus group workshop in the field of building permits. In the test case the focus group consisted of Israeli experts.

The authors present a detailed overview of the as-is building permit process in Israel and the existing challenges. Through this test case, the authors found that the framework is applicable in different countries and that it can provide valuable insights into the core problems within the process. In addition, application of the same framework in different countries can provide comparable results that would allow the authors to identify best practices.

The major contribution of this work is the development and validation of a framework for building permitting process modeling which can be used to identify existing challenges and bottlenecks in the process. Implementing a structured and unified approach provides an opportunity to easily compare processes in different countries to identify best practices.

This study aims to examine Big Data and the management of libraries in the era of the Fourth Industrial Revolution and its implications for policymakers in Nigeria.

A qualitative methodology was used, involving the administration of open-ended questionnaires to librarians from six selected federal universities located in Southwest Nigeria.

The findings of this research highlight that a significant proportion of librarians are well-acquainted with the relevance of big data and its potential to positively revolutionize library services. Librarians generally express favorable opinions concerning the relevance of big data, acknowledging its capacity to enhance decision-making, optimize services and deliver personalized user experiences.

This study exclusively focuses on the Nigerian context, overlooking insights from other African countries. As a result, it may not be possible to generalize the study’s findings to the broader African library community.

To the best of the authors’ knowledge, this study is unique because the paper reported that librarians generally express favorable opinions concerning the relevance of big data, acknowledging its capacity to enhance decision-making, optimize services and deliver personalized user experiences.

The purpose of this paper is to analyse and classify the literature that contributed to three questions, namely, what are the benefits of big data analytics (BDA) in the field of supply chain management (SCM) and logistics, what are the challenges in BDA applications in the field of SCM and logistics and what are the determinants of successful applications of BDA in the field of SCM and logistics.

This paper conducts a systematic literature review (SLR) to analyse the findings of 44 selected papers published in the period from 2016 to 2020, in the area of BDA and its impact on SCM. The designed protocol is composed of 14 steps in total, following Tranfeld (2003). The selected research papers are categorized into four themes.

This paper identifies sets of benefits to be gained from the use of BDA in SCM, including benefits in data analytics capabilities, operational efficiency of logistical operations and supply chain/logistics sustainability and agility. It also documents challenges to be addressed in this application, and determinants of successful implementation.

The scope of the paper is limited to the related literature published until the beginning of Corona Virus (COVID) pandemic. Therefore, it does not cover the literature published since the COVID pandemic.

This paper contributes to the academic research by providing a roadmap for future empirical work into this field of study by summarising the findings of the recent work conducted to investigate the uses of BDA in SCM and logistics. Specifically, this paper culminates in a summary of the most relevant benefits, challenges and determinants discussed in recent research. As the field of BDA remains a newly established field with little practical application in SCM and logistics, this paper contributes by highlighting the most important developments in contemporary literature practical applications.

The purpose of this paper is to investigate the performance of bio-based lubricants (BBL), namely, palm mid-olein (PMO) enriched with an antioxidant agent, tertiary-butylhydroquinone (TBHQ) and a viscosity improver, ethylene-vinyl acetate (EVA), in journal bearing (JB) applications.

Samples of the BBL were prepared by blending it with TBHQ and EVA at various blending ratios. The oxidative stability (OS) and viscosity of the BBL samples were examined using differential scanning calorimetry and a viscometer, respectively. Meanwhile, their performance in JB applications was evaluated through the use of a JB test rig with a 0.5 length-to-diameter ratio at various operating conditions.

It was found that the combination of PMO + TBHQ + EVA demonstrated a superior oil film pressure and load-carrying capacity, resulting in a reduced friction coefficient and a smaller attitude angle compared to the use of only PMO or VG68. However, it was observed that the addition of TBHQ and EVA to the PMO did not have a significant impact on the minimum oil film thickness.

The results would be quite useful for researchers generally and designers of bearings in particular.

This study used PMO as the base stock, and its compatibility with TBHQ and EVA was investigated in terms of its OS and viscosity. The performance of this treated BBL was evaluated in a hydrodynamic JB.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-11-2023-0363/

The purpose of this paper is to present investigations on the significant influence of the tube material and fin density on fluid elastic instability and vortex shedding in a parallel triangular finned tube array subjected to water cross flow.

The experiment was conducted on finned tube arrays with a fin height of 6 mm and fin density of 3 fins per inch (fpi) and 9 fpi. A dedicated setup has been developed to examine fluid elastic instability and vortex shedding. Nine parallel triangular tube arrays with a pitch to tube diameter ratio of 1.78 were considered. The plain tube arrays, coarse finned tube arrays and fine finned tube arrays each of steel, copper and aluminium materials were tested. Plain tube arrays were tested to compare the results of the finned tube arrays having an effective tube diameter same as that of the plain tube.

A significant effect of fin density and tube material with a variable mass damping parameter was observed on the instability threshold. In the parallel triangular finned tube array subjected to water cross flow, a delay in the instability threshold was observed with an increase in fin density. For steel and aluminium tube arrays, the natural frequency is 9.77 Hz and 10.38 Hz, which is close to each other, whereas natural frequency of the copper tubes is 7.40 Hz. The Connors’ stability constant K for steel and aluminium tube arrays is 4.78 and 4.87, respectively, whereas it is 5.76 for copper tube arrays, which increases considerably compared to aluminum and steel tube arrays. The existence of vortex shedding is confirmed by comparing experimental results with Owen’s hypothesis and the Strouhal number and Reynolds number relationship.

This paper’s results contribute to understand the effect of tube materials and fin density on fluid elastic instability threshold of finned tube arrays subjected to water cross flow.

This paper proposes an Information and Communication Technology (ICT) platform to increase the sustainability and resilience of smallholders to face supply chain disruptions in the event of COVID-like crises. The platform facilitates interactions between smallholders and buyers, workers and freight transport companies in agri-food ecosystems. Furthermore, this research work presents the implementation of the freight transport companies’ platform module.

The research work begins with a literature review aiming at analyzing current available ICT solutions supporting smallholders and other actors in the agri-food supply chain. This analysis identifies the research gaps which have to be filled by the platform. Then, the authors proceed with the analysis of the operational scenarios of each platform actor by interacting with experts and operators working in the agri-food sector. The results of such analysis resulted in a comprehensive, unambiguous and consistent set of specification being used to define the platform structure and modules architecture. The platform modules have been developed by using the web-application framework Laravel.

Preliminary tests show that the proposed platform is usable and promises to improve the resilience and economic, social and environmental sustainability of agri-food supply chains, with a focus on smallholders.

The research work allows players in the agri-food supply chain and in particular small local producers to react and mitigate the impact of COVID-like crises through development of a platform in which smallholders, citizens (buyers and workers) and freight transport companies are simultaneously present.