Industry Knowledge Research Articles

How to Show You're a "Good Fit" During an Interview

This article discusses strategies for job applicants to effectively demonstrate cultural fit during the interview process. It emphasizes the importance of fit in today's competitive hiring environment, as employers seek candidates who will seamlessly integrate into their team and advance organizational goals. The article outlines ways for applicants to research the company and role thoroughly, align examples of their background to the employer's stated values, highlight enthusiasm for the position and company, provide substantive examples to evidence a compatible work style, and showcase relevant industry knowledge. It presents real examples of how candidates have persuasively shown cultural fit. Overall, the article provides research-based guidance on customizing stories and responses during interviews to communicate passion and compatibility through tangible scenarios that leave hiring managers confident in an applicant's long-term potential for success within their unique culture.

Strategizing Originality for Technology Convergence: An Engineering Management Approach

This study examines how the integration of internal expertise with external research partnerships influences the confluence of technologies. Analyzing a decade’s patents, it clarifies the efficacy of varied knowledge acquisition strategies on innovation. The research quantifies the contribution of such synergies to innovation across technological, industrial, and geographic realms. Methodologically, it employs patent analytics to dissect originality into technology scope, assignee variety, industry diversity, and country-specific innovation trends from 2010 to 2019. Findings reveal a positive link between comprehensive technological and industrial knowledge and enhanced technological convergence. Significantly, it accentuates the role of global collaboration in unifying new technologies and industries. This interdisciplinary approach and in-depth organizational learning understanding are vital for spurring innovation. The results offer pivotal insights for academia and policy formulation in technology governance, advocating for integrated knowledge strategies to drive sector-spanning technology unification. The study provides a blueprint for fostering innovation through inter-sectoral and interdisciplinary exchange.

Constructing a Coal Mine Safety Knowledge Graph to Promote the Association and Reuse of Risk Management Empirical Knowledge

Coal mining production processes are complex and prone to frequent accidents. With the continuous improvement of safety management systems in China’s coal mining industry, a vast amount of coal mine safety experience knowledge (CMSEK) has been accumulated, originating from on site operations. This knowledge has been recorded and stored in paper or electronic documents but it remains unconnected, and the increasing volume of documents further complicates the reuse and sharing of this knowledge. In the era of large models and digitalization, this knowledge has yet to be fully developed and utilized. To address these issues, a risk management checklist was derived from coal mining site data. By integrating intelligent algorithm models and the coal industry knowledge engineering design, a coal mine safety experience knowledge graph (CMSEKG) was developed to enhance the efficiency of utilizing coal mine safety experience knowledge. Specifically, we creatively developed a coal mine safety experience knowledge representation framework, capable of representing coal mine risk inspection records from different sources and of various types. Furthermore, we proposed a deep learning-based coal mine safety entity recognition model (CMSNER), which can effectively extract coal mine safety experience knowledge from text. Finally, the CMSEKG was stored using the Neo4j graph database, and a knowledge graph was constructed using selected case information as examples. The CMSEKG effectively integrates fragmented safety management experience and professional knowledge, promoting knowledge services and intelligent applications in coal mining operations, thereby providing knowledge support for the prevention and management of coal mine risks.

A smart agriculture information system delivering research data for adoption by the Australian grains industry

Online Farm Trials (OFT) is a bespoke online information system providing current and historical grains trials research data and information in Australia. It represents a technology that supports smart agriculture by enabling discoveries in the data. Its impact on users has been established, supporting industry knowledge and decision making and leading to improved work practices. The aim of this research is to identify the contribution of OFT, as a tool that supports smart agriculture, and to explore current adoption, barriers and the opportunities for expansion of the system in the Australian grains industry, as perceived by industry experts. In-depth, semi-structured interviews were conducted with leaders and innovators from the agriculture industry (N = 16). Thematic analysis of the qualitative data reveals widespread value of OFT as an important resource for accessing industry-dedicated, current and historical research trials data. Barriers to adoption were identified which included incomplete data, and a lack of industry-wide awareness. Opportunities to consolidate and expand the data, together with improved industry awareness, are essential to improving industry-wide adoption. Key learnings were identified in the research that has benefit for the industry, and consideration for the adoption of technology in smart agriculture. This includes functionality, audience, completeness of information/data, consideration of new technologies and the promotion of the system supported by real world examples to encourage industry adoption. Recommendations to support the design and development of digital platforms in agriculture, in the future, are also offered.

Ship recycling process in Bangladesh and a survey-based risk assessment with mitigation proposal

Ship recycling industries play a significant role in the economic development of South Asian countries, including Bangladesh. In the wake of entering the Hong Kong Convention (HKC) in June 2025, the industry aims to improve its overall safety standards cost-effectively. However, due to the restrictive nature of the industry, solutions based on field surveys are limited. This study successfully articulates the indigenous industry knowledge of the recycling process in Bangladesh after carefully studying the practices followed by the existing ship recycling yards in the country. Based on field surveys conducted across 18 ship recycling yards in Bangladesh, the risk patterns inherent in the ship recycling industry are studied; for example, highly hazardous tasks in the ship recycling process are found to be (i) explosion hazards during primary cutting operations at the intertidal zone, (ii) falling hazards during inspections at the outer anchorage, (iii) falling and crushing hazards during block cutting and handling heavy objects. Industry experts and scholars consider the perception of the risk pattern regarding the current practices of ship recycling activities to cover a broader perspective of this issue. The correlation of survey outcomes with actual accident cases is comparable. Finally, practical recommendations for minimising risks have been recommended, especially in high-risk workplace areas. The study adds knowledge to the existing literature by giving a clear picture of evidence-based findings for this sector and helps improve the industry's overall safety standard.

Seven Critical Attributes for Selecting an NDC API Partner in the Airline Industry

This article examines seven critical attributes that airlines should consider when selecting an NDC API partner for enhancing their distribution capabilities. These attributes include technical skill, airline industry knowledge, project management experience, travel industry market knowledge, quality and operational excellence, NDC certification, and futureproofing capabilities. By evaluating potential partners based on these criteria, airlines can ensure successful NDC implementation and establish strategic distribution partnerships. The article provides insights into each attribute, highlighting their importance in the context of the evolving airline distribution landscape and the challenges associated with NDC adoption. It emphasizes the significance of choosing the right partner to navigate the complexities of modern distribution, optimize offer management, and ultimately deliver superior value to customers

Evaluating geospatial education provision: a case study of Aotearoa New Zealand

ABSTRACT Geospatial technology is still a growth area and knowledge of these technologies remains essential for a skilled workforce. In higher education, geospatial curriculum design is predominantly determined by educators and is influenced by what educators believe should be taught. Knowledge of national or regional provision can support a richer understanding of expected provision and areas of over or under-provision to better support the needs of varied geospatial roles and identify possible programme specialisations. In this paper, we describe an approach for evaluating Aotearoa New Zealand’s provision of geospatial education in the tertiary education sector. First, we examine and compare current course offerings in New Zealand’s tertiary sector against selected institutions in five countries using the GIS&T Body of Knowledge and Geospatial Technology Competency Model as a framework for comparison. Second, we evaluate the results of a focus-group workshop of geospatial education providers and geospatial industry stakeholders about perceived needs in geospatial education in New Zealand. We find this approach works well to support a better understanding of what is provided for both educators and industry. By including industry geospatial professionals in identifying perceived gaps, educators’ knowledge of industry needs will grow as well as industry’s understanding of current provision.

Process mining-enhanced quality management in food processing industries

PurposeThe purpose of this study is to present a methodical procedure on how to prepare event logs and analyse them through process mining, statistics and visualisations. The aim is to derive roots and patterns of quality deviations and non-conforming finished products as well as best practice facilitating employee training in the food processing industry. Thereby, a key focus is on recognising tacit knowledge hidden in event logs to improve quality processes.Design/methodology/approachThis study applied process mining to detect root causes of quality deviations in operational process of food production. In addition, a data-ecosystem was developed which illustrates a continuous improvement feedback loop and serves as a role model for other applications in the food processing industry. The approach was applied to a real-case study in the processed cheese industry.FindingsThe findings revealed practical and conceptional contributions which can be used to continuously improve quality management (QM) in food processing. Thereby, the developed data-ecosystem supports production and QM in the decision-making processes. The findings of the analysis are a valuable basis to enhance operational processes, aiming to prevent quality deviations and non-conforming finished products.Originality/valueProcess mining is still rarely used in the food industry. Thereby, the proposed method helps to identify tacit knowledge in the food processing industry, which was shown by the framework for the preparation of event logs and the data ecosystem.

Development of A Workplace Learning Model to Improve Skills Competency of Vocational School Students

This research aims to develop a workplace learning model to improve the catering skills of tourism vocational school students by building a conceptual foundation that considers the industrial situation. This development research uses Design-Based Research (DBR) comprising planning, learning interactions, and evaluation stages for students, supervisors, and instructors. This study analyzes data using qualitative and quantitative descriptive methods. This research produces industrial practical learning tools (WPL) in the form of guidebooks for teachers, instructors and students. How to study in industry, the implementation process, ideal criteria, and completion of study contracts are outlined. Increasing student catering skills competency through industrial learning based on a measurable field approach. How students can absorb, adopt tacit and explicit knowledge in industry is a value in itself. Research findings and concepts can be used for further development so that the quality of vocational education graduates can be considered.

“Magical math hand‐waving”

AbstractPolitical risk forecasting is an industry that provides specialized analysis to a range of clients, including insurance companies, extractive industries, governments, defense ministries, and NGOs. Risk forecasters aim to help their clients mitigate risks by anticipating political developments that could threaten their investments and assets, especially in the “emerging markets” of the Global South. What can ethnographic analysis reveal about this industry's knowledge practices and how it transforms open‐source intelligence into commercially relevant risk forecasts? Based on five years of work experience as Senior Africa Analyst at a commercial intelligence firm, I examine how forecasters select and process risk‐relevant events, narrate them as qualitative risk briefs, and finally assign them quantitative risk scores. Through a close reading of a risk firm's internal guidelines and its daily practices, I show how analysts construct risk in African countries in line with entrenched sociopolitical imaginaries, reproducing the coloniality of finance.

MSIDetector: Detecting Multi-Scenario industrial defects using an adapted visual foundation model and dual thresholding discriminator

Industrial defect detection is crucial for enhancing product quality. The diverse nature of industrial scenarios has posed challenges for developing a unified defect detection model that can address multiple industrial scenarios. To meet this challenge, we started from a data perspective, developing three self-designed data acquisition apparatuses to capture defect samples from multiple industrial scenarios. Leveraging grounding DINO (GDINO), an open-set detection approach, we propose a unified defect detection method, MSIDetector. By integrating an industrial feature adapter and a context-aware dual-thresholding defect discriminator, we successfully incorporate prior industrial knowledge into the model. The performance of the MSIDetector was evaluated across four industrial scenarios, including a public defect dataset, achieving a total state-of-the-art mAP@50 of 76.2, surpassing the performance of FasterRCNN, YOLO-7X, and the visual foundation model GDINO, with quantitative increases of 13.93, 6.55, and 2.1, respectively. This report represents the first successful attempt to apply foundation models to defect detection.

263 Public perspectives of calf handling: A survey of Canadians

Abstract In North American beef production handling and restraint of young calves is integral to animal welfare and management practices. This study used a mixed method approach to gather public perceptions of three handling and restraint methods common on beef operations in western Canada (RW - Roping and Wrestling, NF – roping and NordForks, and TT – Tilt Table). Canadian participants (n = 551) recruited by CloudResearch to represent the national demographics participated in an online survey that included videos of each handling method to ascertain preferences and acceptability. Prior to watching the videos, participants were given industry information about handling and restraint or generic information regarding hay as a control information statement within the topic of agriculture. The survey also collected information about knowledge of the beef industry, animal welfare, and empathy toward animals. The reasons for preferences were described as presence of a perceived positive attribute and absence of a perceived negative for most preferred methods, and inversely when explaining least preferred method. The findings focused on calf’s experience, perception of handler actions, and pragmatic balancing of handler needs and a good life for the calf. Methods were rated as more acceptable for participants that ate meat consistently, knew more about the beef industry, and, to a lesser extent, if the individual had a lower animal empathy score (Table 1). Acceptability was not affected by providing information about the practices; however, information about handling and restraint did elicit more pragmatic reasoning. Most participants preferred TT over NF and RW (P < 0.001) and found TT more acceptable as well (P < 0.001). The TT was the most preferred method due to calf experience and human handling, notably the absence of dragging a calf, which was predominant in why participants selected NF or RW as their least preferred method. Consistency of themes highlights that the Canadian public has a fundamental expectation of value for the quality of life of the calf, humane handling, and pragmatism which aligns with beef sustainability initiatives and represent points of connection for building trust between industry and the public.

Disadvantages and Professional Ethics Issues Arising from the Professionalism of Registered Architect Qualifications

Professionalism is an important criterion for judging an industry. It is not only closely linked to the development of the industry, but also closely linked to ethics and trust between users. This article will focus on an important manifestation of professionalism in the construction industry: registered architect qualifications. Discuss the professionalism of trust and ethics, and the drawbacks of professionalism in the qualification of architects. Since Registered Architect's Certificate is a professional proof of the construction industry. It represents an architect with a wealth of relevant industry knowledge and practical experience, representing a high ethical standard in the construction industry and trusted by society and users. It is an important part of the development of the construction industry. However, behind the professionalism represented by the registered architect qualifications, there are still some shortcomings, such as morality, trust and skills, and threaten the professional development of the construction industry. The purpose of this article is to discuss the shortcomings behind the professionalism of a registered architect qualification. Discuss the harmful in professionalism and ethics behind the registered architect qualifications, and how to avoid these hazards by modifying regulations in this field.

Exploring Industrial Engineering Knowledge and Environmental Sustainability

This research identifies the relationship between industrial engineering and environmental sustainability knowledge components. A combination of a systematic literature review (SLR) and applied thematic analysis (ATA) is employed to uncover the pertinent literature associated with the purpose of this research. Whilst various forms of strategies, theories, methods, and practices were uncovered in each of the knowledge components, only a few were overlapping. These overlapping components include green supply chain operations, circular economy, and technology management. This study is the first in a series of studies contextualising industrial engineering knowledge in terms of its applicability to environmental sustainability. These results reveal concepts from industrial engineering and environmental sustainability knowledge components that can be used to systematically design methodologies or practically implement them in an industry or organisation. Methods and practices were a dedicated theme in the analysis, and these can be used by practitioners. A circle packing diagram is crafted using the IISE Body of Knowledge as a means of categorisation. This study considered industrial engineering as a catalyst in creating new forms of transdisciplinary knowledge areas. It also considers how industrial engineering knowledge can contribute to meeting environmental challenges.

Exploring the maritime frontier: unveiling the transformative power of work-based learning (WBL) in maritime education and training (MET)

PurposeTo explore the benefits and challenges of WBL in MET in Thailand.Design/methodology/approachA qualitative approach was used, involving interviews and surveys with participants from MET.FindingsWBL offers practical experience, improved employability, networking opportunities, and real-world problem-solving skills, industry knowledge, professional development, and better learning outcomes, but faces challenges like language barriers, environmental issues, limited placement availability, time limits and task management, safety concerns, limited resources, and assessment issues.Originality/valueProvides insights into WBL in the Thailand maritime sector and highlights areas for improvement to enhance student preparedness for industry roles.

Investigating the relationship between knowledge of the media industry and media trust in a government-owned and government-controlled media system

Investigating the relationship between knowledge of the media industry and media trust in a government-owned and government-controlled media system

A multi-hierarchical aggregation-based graph convolutional network for industrial knowledge graph embedding towards cognitive intelligent manufacturing

The rapid development and widespread applications of cognitive computing technologies have led to a paradigm shift towards cognitive intelligent development in manufacturing, where knowledge plays an increasingly important role in enabling higher levels of cognition. Knowledge graph (KG) has emerged as one of the essential tools in cognitive intelligent manufacturing and its completion would significantly impact the quality of knowledge. To facilitate effective knowledge management, KG embedding has proven to be an effective approach for KG completion. However, existing models have deficiencies in achieving relation-specific transformations, differentiating the neighbor nodes, and exploiting the intermediate information generated during the KG embedding learning process, which is prone to limit model performance and hinder successful applications. To address these limitations, this paper proposes a new multi-hierarchical aggregation-based graph convolutional network (GCN), consisting of relation-aware, entity-aware, and across-block aggregation. A parallel relation and entity-aware aggregation (PREA) block is established to simultaneously perform relation-specific transformations and entity-differentiated learning. Additionally, an across-block aggregation is constructed to efficiently integrate extracted information from the intermediate stacked block. To demonstrate the effectiveness and superiority of the proposed approach, 3D printing KG is constructed, which is a representative knowledge-intensive industry linking to a variety of aspects like raw materials, adhesion, usages, etc. Extensive experiments are conducted based on the link prediction task. Illustrative examples are provided to reveal the practical implementation of the proposed method, along with technical details and insightful opinions, exhibiting its promising applications.

Model Safety Behaviour untuk Mengurangi Accident dalam Menjaga Keberlanjutan Industri Pariwisata Madura

The tourism industry is a key player in the global economy, generating billions of dollars in revenue and providing jobs for millions of people around the world, contributing more than $6 trillion annually, employing more than 250 million people, and generating 7% of global GDP (World Travel Tourism Council, 2018). However, as the industry grows, comes the responsibility to ensure the safety and well-being of employees and tourist visitors. Safety is critical in maintaining the reputation and sustainability of the tourism industry, while employee safety is critical in ensuring a productive and positive work environment. In recent years, there has been growing concern about safety practices in the tourism industry, prompting researchers to investigate the factors that contribute to safe behavior among employees. This research focuses on the impact of safety knowledge and safety behavior in the tourism industry so that it has an impact on reducing the number of accidents. The aim of this research is to provide insights that can help improve safety practices and ensure a positive and safe experience for employees and tourists at destinations in Madura and reduce accident incidents.

Manufacturing resilience through disruption mitigation using attention-based consistently-attributed graph embedded decision support system

With global supply chains experiencing significant disruptions, there is increasing emphasis on enhancing manufacturing resilience by mitigating supply chain-propagated impacts on production entities in a holistic manner. However, existing mitigative strategies typically rely on implicit experience, which may be biased and suboptimal. Furthermore, the lack of consideration towards the chain of custody, multi-disruption management, and transportation networks results in unintuitive systems. To overcome these challenges, this study proposes a graph embedding-based mitigation decision support system (GEM-DSS) using a heterogeneous industrial knowledge graph to facilitate disruption management. Addressing the prevailing industrial challenge of knowledge representations and graph incompleteness, an attention-based consistently-attributed graph embedding (ACAGE) model is proposed, with the capability to learn and factor in multi-attributes and multi-structures within the knowledge graph. Through supplier selection and material substitution mitigation strategies, two computational pipelines are designed as part of a dual-stage search mechanism to generate feasible solutions. The experimental results of the ACAGE model for knowledge graph completion outperform several attributed graph embedding baselines, achieving 0.999 AUC-ROC and 0.990 AUC-PR on the constructed real-world industrial knowledge graph. The functionality and applicability of the GEM-DSS are demonstrated in an automotive case study with dynamic considerations via a cognitive computing methodology. This study aims to provide valuable insights for both academia and industry towards enabling manufacturing continuity in the face of global supply chain challenges.

Factors Influencing HealthTech Startup Firm Outcomes: Does Founder / CEO Industry Knowledge Matter?

Factors Influencing HealthTech Startup Firm Outcomes: Does Founder / CEO Industry Knowledge Matter?