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The purpose of this paper is to use the corresponding magnetic sensor and detection method to detect and image the defects of small diameter pipelines. Urban gas pipeline is an energy transportation tool for urban industrial production and social life, which is closely related to urban safety. Preventing the occurrence of urban gas pipeline transportation accidents and carrying out pipeline defect detection are of great significance for the urban economic and social stability. To perform pipeline defect detection, the magnetic flux leakage internal detection method is generally used in the detection of large-diameter long-distance oil and gas pipelines. However, in terms of the internal detection of small-diameter pipelines, due to the heavy weight, large structure of the detection device and small pipe diameter, the detection is more difficult.

In order to solve the above matters, self-made three-dimensional magnetic sensor and three-dimensional magnetic flux leakage imaging direct method are proposed for studying the defect identification. Firstly, for adapting to the diameter range of small-diameter pipelines, and containing the complete information of the defect, a self-made three-dimensional magnetic sensor is made in this paper to improve the accuracy of magnetic flux leakage detection. And on the basis of it, a small diameter pipeline defect detection system is built. Secondly, as detection signal may be affected by background magnetic field interference and the jitter interference, the complete ensemble empirical mode decomposition with adaptive noise method is utilized to screen the detected signal. As a result, the useful signal is reconstructed and the interference signal is removed. Finally, the defect contour inversion imaging of detection is realized based on the direct method of three-dimensional magnetic flux leakage imaging, which includes three-dimensional magnetic flux leakage detection data and data segmentation recognition.

The three-dimensional magnetic flux leakage imaging experimental results shown that, compared to the actual defects, the typical defects, irregular defects and crack groove defects can be analyzed by the magnetic flux leakage defect contour imaging method in qualitative and quantitative way respectively, which provides a new idea for the research of defect recognition.

A three-dimensional magnetic sensor is made to adapt the diameter range of small diameter pipeline, and based on it, a small-diameter pipeline defect detection system is built to collect and display the magnetic flux leakage signal.

This study illustrates the economic and financial dynamics of the sector, analysing the evolution of the main ratios of profitability and financial structure of 1,559 Italian real estate companies divided into the three macro-regions: North, Centre and South, in the period 2011–2020. In this way, it is also possible to verify the responsiveness to the 2020 pandemic crisis.

The analysis uses descriptive statistics tools and the ANOVA method of analysis of variance, supplemented by the Tukey–Kramer test, to identify significant differences between the three Italian macro-regions.

The study shows the increase in profitability after the 2008 crisis, despite its reverberation in the years 2012–2013. The financial structure of companies improved almost everywhere. The pandemic had modest effects on performance.

In the future, other indices should be considered to gain a more comprehensive view. This is a quantitative study based on financial statements data that neglects other important economic and social factors.

Public policies could use this study for better interventions to support the sector. In addition, internal management can compare their company's performance with the industry average to identify possible improvements.

The research analyses an economic field that employs a large number of people, especially when considering the construction and real estate services covered by this analysis.

The study contributes to the literature by providing a quantitative analysis of industry dynamics, with comparative information that can be deduced from financial statements over the years.

The purpose of this study is to establish a method for accurately extracting torch and seam features. This will improve the quality of narrow gap welding. An adaptive deflection correction system based on passive light vision sensors was designed using the Halcon software from MVtec Germany as a platform.

This paper proposes an adaptive correction system for welding guns and seams divided into image calibration and feature extraction. In the image calibration method, the field of view distortion because of the position of the camera is resolved using image calibration techniques. In the feature extraction method, clear features of the weld gun and weld seam are accurately extracted after processing using algorithms such as impact filtering, subpixel (XLD), Gaussian Laplacian and sense region for the weld gun and weld seam. The gun and weld seam centers are accurately fitted using least squares. After calculating the deviation values, the error values are monitored, and error correction is achieved by programmable logic controller (PLC) control. Finally, experimental verification and analysis of the tracking errors are carried out.

The results show that the system achieves great results in dealing with camera aberrations. Weld gun features can be effectively and accurately identified. The difference between a scratch and a weld is effectively distinguished. The system accurately detects the center features of the torch and weld and controls the correction error to within 0.3mm.

An adaptive correction system based on a passive light vision sensor is designed which corrects the field-of-view distortion caused by the camera’s position deviation. Differences in features between scratches and welds are distinguished, and image features are effectively extracted. The final system weld error is controlled to 0.3 mm.

Economic costs and benefits are at the core while taking decision to adopt digitalization in the supply chain. The present chapter provides an in-depth exploration of the economic dimensions of digital supply chain management (DSCM) adoption in a firm. Drawing from a diverse source of literature, this chapter discusses the effect of economic outlook on DSCM adoption, the economic benefits of DSCM adoption and costs associated with it, and economic analysis and evaluation methodologies. The chapter also shares the case studies illustrating the real-world implications of economic considerations within DSCM initiatives. The chapter highlights how changing international socioeconomic and political dynamics can influence businesses across the globe. By analyzing the impacts of evolving market trends, changing consumer preferences, and geopolitical tensions, organizations can considerably forecast the possible impacts of these macroeconomic forces adeptly. The chapter also undertakes discussion on the economic cost and benefits associated with DSCM adoption. The economic analysis helps understand that the expected benefits outweigh economic costs, substantiating the economic viability of DSCM projects. The chapter concludes by discussing the examples of some real-world companies, highlighting how organizations have successfully applied economic analyses to their DSCM initiatives. This also highlights as to how showcasing how detailed economic assessments can justify substantial investments, deliver operational efficiencies, and reshape industries.

This study aims to examine the effect of several factors on Muslims' intentions to invest in the Hajj fund Sukuk, Jordan. The study's hypothesis and model were derived from previous studies.

The present study was undertaken based on a self-administered questionnaire of 356 Jordanians who are Muslims and non-investors in Hajj Fund Sukuk. The sample was selected using a purposive sampling method. The data were analyzed using Smart-PLS version 4.

The results indicated that social influence, knowledge, religion and return on investment significantly affect the purchase intention of Jordanian Muslims to invest in Hajj Fund Sukuk.

There are some limitations to this study. First, the study was done in Jordan; thus, additional research might be conducted in other parts of the Islamic world to learn more about the perception of investing in Islamic Sukuk, particularly Hajj Sukuk. Second, while the present study used a quantitative research technique to achieve its purpose, it would be advantageous if the researchers used more qualitative techniques, such as interviews or focus groups, in the future to explore additional factors that may impact Muslims' intent to invest in Hajj Fund Sukuk.

The findings of the current study could help practitioners in the Islamic sukuk industry by identifying the key factors that encourage Muslims to invest in Hajj sukuk. They may use the results of this study in the formulation of marketing policies and the development of marketing strategies to persuade more investors to invest their money in these sukuk.

To the best of the author’s knowledge, this is the first study carried out to better understand the main factors that may influence Muslims to invest in Hajj Sukuk in the Jordanian context. Hence, this study contributes to increasing the body of knowledge in the area of Islamic marketing in general and in the field of Islamic sukuk investment specifically.

Open innovation (OI), by now one of the major concepts for the analysis of innovation, is seen as a methodology for collaboratively designing and implementing solutions by engaging stakeholders in an iterative and inclusive service design process. This paper aims to empirically investigate OI capacities, defined as a cooperative, knowledge-sharing innovation ecosystem, and to explore how it can lead to improved performance of firms in Central and Eastern European (CEE) and Southeastern European (SEE) countries.

The study builds on the World Bank/European Bank for Reconstruction and Development (EBRD’s) Business Environment Enterprise Performance Survey (BEEPS) dataset for 2009, 2013 and 2019. Primarily, the research model was estimated using log-transformed ordinary least squares (OLS). Taking into consideration that this method might produce substantial bias, yielding misleading inferences, this study is fitting Poisson pseudo maximum likelihood estimators with robust standard errors and instrumental variable/generalized method of moments estimation (IV/GMM) approach for comparative results. Secondarily, the research model was tested using structural equation modelling (SEM) to investigate the relationship between five OI capacities and firm performance.

The findings indicate that there is a significant positive relationship between most OI capacities and firm performance, except for innovation, which did not show a statistically significant relationship with firm performance. Specifically, research and development (R&D), knowledge and coopetition are statistically significant and positively associated with firm performance, whereas transformation is statistically significant but negatively associated with firm performance. The IV/GMM estimations’ findings support the view that the firm performance is significantly affected by OI capacities, together with some control variables such as size, age, foreign ownership and year dummy to have a significant impact on firm performance.

This paper fills an identified gap in the literature by investigating the impact of OI on firm performance executed in the specific CEE and SEE country context.

Introduction: Historically, the corporate governance (CG) framework was designed primarily to safeguard the economic interests of shareholders, as a result of political and legal interventions, developing into an effective instrument for stakeholders and society in general.

Purpose: The core objectives of the study include: identifying journals/publications responsible for publishing CG studies in India, key CG issues covered by CG researchers, the amount of high-impact CG literature across different time periods, sectors/industries covered by CG researchers and different research instruments (quantitative or qualitative) used in CG studies in India.

Design/methodology: The chapter used a sample of 130 corporate governance studies that fulfil the selection criteria, drawn from the repository of over 100 reputed journals that are either recognised by the Australian Business Deans Council (ABDC) or indexed by SCOPUS. A systematic literature review has been carried out pertaining to CG issues in India, based on various statistical tools, data, industries, research outlets & citations, etc.

Findings: The results show an overwhelming number of studies have assessed the relationship between CG variables and firm performance, which could be measured through a variety of performance metrics such as ROA and ROI. Apart from empirical analysis, many conceptual studies use repetitive basic statistical tools like descriptive statistics or regression analysis. The chapter offers insights into current achievements and future development.

Originality/value: This bibliometric study is a useful guide for policymakers, corporate leaders, research organisations and management faculty to draw insights from work produced by eminent researchers in GC in India.

Design Science (DS) is a relatively new paradigm for addressing complex real-world problems through the design and evaluation of artifacts. Its constituent methodologies are currently being discussed and established in numerous related research fields, such as information systems and management (Hevner et al., 2004). However, a DS methodology that describes the “how to” is largely lacking, not only in the field of OM but in general. The Theory of Constraints (TOC) and its underlying thinking processes (TP) have produced several novel artifacts for addressing ill-structured real-world operations problems (Dettmer, 1997; Goldratt, 1994), but they have not been analyzed from a DS research standpoint. The purpose of this research is to demonstrate how TOC’s thinking process methodology can be used for conducting exploratory DS research in Operations and Supply Chain Management (OSCM).

A case study of spare parts replenishment illustrates the use of TOC’s thinking processes in DS to structure an initially unstructured problem context and to facilitate the design of a novel solution.

TOC’s thinking processes are an effective methodology for problem-solving DS research, enabling the development of novel solutions in initially unstructured and wicked problem situations. Combined with structured CIMO design logic TOC’s thinking process offers a systematic method for exploring wicked problems, designing novel solutions, and demonstrating theoretical contributions.

The implication for research is that TOC’s thinking process methodology can provide important elements of the lacking “how to” methodology for DS research, not only for the field of OM but in general for the field of management.

The practical outcome of the research is a novel design for dynamic buffer-based replenishment that extends beyond organizational boundaries.

This work shows how the thinking processes can be used in DS research to develop rigorous design propositions for ill-structured problems.

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.

The main aim of this study is to identify and prioritize the factors that influence the adoption of big data analytics (BDA) within the supply chain (SC) of the food industry in India.

The study is carried out in two distinct phases. In the first phase, barriers hindering BDA adoption in the Indian food industry are identified. Subsequently, the second phase rates/prioritizes these barriers using multicriteria methodologies such as the “analytical hierarchical process” (AHP) and the “fuzzy analytical hierarchical process” (FAHP). Fifteen barriers have been identified, collectively influencing the BDA adoption in the SC of the Indian food industry.

The findings suggest that the lack of data security, availability of skilled IT professionals, and uncertainty about return on investments (ROI) are the top three apprehensions of the consultants and managers regarding the BDA adoption in the Indian food industry SC.

This research has identified several reasons for the adoption of bigdata analytics in the supply chain management of foods in India. This study has also highlighted that big data analytics applications need specific skillsets, and there is a shortage of critical skills in this industry. Therefore, the technical skills of the employees need to be enhanced by their organizations. Also, utilizing similar services offered by other external agencies could help organizations potentially save time and resources for their in-house teams with a faster turnaround.

The present study will provide vital information to companies regarding roadblocks in BDA adoption in the Indian food industry SC and motivate academicians to explore this area further.