Search results

This study aims to explore and empirically test variables influencing material delivery schedule inaccuracies?

A mixed-method case approach is applied. Explanatory variables are identified from the literature and explored in a qualitative analysis at an automotive original equipment manufacturer. Using logistic regression and random forest classification models, quantitative data (historical schedule transactions and internal data) enables the testing of the predictive difference of variables under various planning horizons and inaccuracy levels.

The effects on delivery schedule inaccuracies are contingent on a decoupling point, and a variable may have a combined amplifying (complexity generating) and stabilizing (complexity absorbing) moderating effect. Product complexity variables are significant regardless of the time horizon, and the item’s order life cycle is a significant variable with predictive differences that vary. Decoupling management is identified as a mechanism for generating complexity absorption capabilities contributing to delivery schedule accuracy.

The findings provide guidelines for exploring and finding patterns in specific variables to improve material delivery schedule inaccuracies and input into predictive forecasting models.

The findings contribute to explaining material delivery schedule variations, identifying potential root causes and moderators, empirically testing and validating effects and conceptualizing features that cause and moderate inaccuracies in relation to decoupling management and complexity theory literature?

This paper explores Lean Six Sigma principles and the DMAIC (define, measure, analyze, improve, control) methodology to propose a new Lean Six Sigma 4.0 (LSS 4.0) framework for employee occupational exams and address the real-world issue of high-variability exams that may arise.

This study uses mixed methods, combining qualitative and quantitative data collection. A detailed case study assesses the impact of LSS interventions on the exam management process and tests the applicability of the proposed LSS 4.0 framework for employee occupational exams.

The results reveal that changing the health service supplier in the explored organization caused a substantial raise in occupational exams, leading to increased costs. By using syntactic interoperability, lean, six sigma and DMAIC approaches, improvements were identified, addressing process deviations and information requirements. Implementing corrective actions improved the exam process, reducing the number of exams and associated expenses.

It is important to acknowledge certain limitations, such as the specific context of the case study and the exclusion of certain exam categories.

The practical implications of this research are substantial, providing organizations with valuable managerial insights into improving efficiency, reducing costs and ensuring regulatory compliance while managing occupational exams.

This study fills a research gap by applying LSS 4.0 to occupational exam management, offering a practical framework for organizations. It contributes to the existing knowledge base by addressing a relatively novel context and providing a detailed roadmap for process optimization.

This study aims to explore information-seeking anxiety (ISA) among undergraduate, postgraduate and PhD students at Dr Babasaheb Sawant Konkan Krishi Vidyapeeth, located in Dapoli, Distt. Ratnagiri, Maharashtra. The research focuses on evaluating the intensity of ISA, understanding its underlying causes and identifying potential strategies to reduce its effects, particularly within the context of an agricultural university.

This research intends to examine Information seeking anxiety (ISA) among undergraduate, postgraduate and PhD students at Dr Babasaheb Sawant Konkan Krishi Vidyapeeth located in Dapoli District, Ratnagiri, Maharashtra. This research intends to examine and investigate the intensity of ISA among students at an agriculture university, its root causes and possible methods to reduce its effects. The research adopts quantitative surveys with qualitative secondary sources to achieve a comprehensive understanding of ISA among students. A standardized scale was used as the primary data collection tool to assess participants’ levels of ISA. A total of 122 students participated in the study, completing the survey and providing valuable data for analysis.

The findings reveal a range of ISA levels among students, indicating the presence of varied information skills and abilities. This underscores the need for well-structured information literacy programs tailored to address these disparities. A significant gender-based difference in ISA was observed, with female students exhibiting lower anxiety levels in relation to “integrated elements 3” at a significance level of .05 (t = 1.327). Interestingly, the study found that the students' rural or urban backgrounds did not significantly impact their ISA levels. Additionally, various integrated elements within the information seeking anxiety scale were found to be interconnected, contributing to the overall anxiety experienced by students.

The study highlights the importance of designing and implementing targeted interventions to address ISA in academic settings. These findings suggest that tailored information literacy programs could be effective in reducing ISA among students. Future research could build on these results by examining ISA across different disciplines and institutions, thereby contributing to the development of broader, more generalized strategies for mitigating ISA in diverse educational contexts. Individuals may not accurately recall or report their anxiety levels, potentially affecting the study’s findings. This study’s findings may not be generalizable to all agriculture university users due to factors such as the specific university setting, student demographics and agricultural disciplines represented.

This study provides actionable insights for academic institutions, particularly agricultural universities, in developing targeted information literacy programs. These programs can be designed to address varying levels of ISA among students, enhancing their ability to effectively seek and use information. Additionally, understanding the gender differences in ISA can guide the creation of more inclusive resources and support systems, ultimately improving the overall academic experience and reducing anxiety-related barriers to learning.

The research highlights the broader societal impact of ISA, particularly how it can affect students’ academic success and overall well-being. By addressing ISA through tailored interventions, institutions can contribute to reducing educational inequalities, especially in rural and underrepresented communities. This can foster a more equitable learning environment where all students, regardless of their background, have the opportunity to thrive academically and socially.

This study contributes to the relatively underexplored area of ISA within the context of agricultural universities. By combining quantitative and qualitative approaches, the research offers a comprehensive understanding of ISA, its causes and potential solutions. The findings provide new insights into how gender and integrated elements of ISA influence anxiety levels, offering valuable contributions to the field of library and information science, particularly in specialized educational settings like agricultural universities.

The purpose of the study is to examine how risk factors contribute to the occurrence of defects in a process. By analyzing these risk factors in relation to process quality, the study aims to help organizations prioritize their resources and efforts toward addressing the most significant risks. These challenges, integrated with the emerging concept of Quality 4.0, necessitate a comprehensive risk assessment technique.

Fuzzy logic integrated with an analytic network process is used in the process failure mode and effects analysis for conducting risk identification and assessment under uncertainty. Through a mathematical model, the linkage of risk with Six Sigma is established and, finally, a value–risk matrix is developed for illustrating and analysing risk impact on process quality.

A case study on fused filament fabrication demonstrates the proposed methodology’s applicability. The results show its effectiveness in assessing risk factors’ impact on Six Sigma metrics: defects per million opportunities/sigma level.

By integrating qualitative assessments and leveraging available data, this approach enables a more comprehensive understanding of risks and their utilization for an organization’s quality improvement initiatives.

This approach establishes a risk-centric Six Sigma assessment method in accordance with the requirement of ISO 9001:2015 and in the context of Quality 4.0.

This paper aims to explore the extent, nature and communication potential of companies’ use of three popular social media platforms (Facebook, X and LinkedIn) to report on sustainability.

Qualitative methodology through the use of the netnography approach was adopted to evaluate the use of social media for sustainability communication by the Top 50 ASX companies. Content analysis of all company posts determined those with social and environmental content. A thematic analysis was performed using the global reporting initiative (GRI) framework to examine the nature of the reporting. The media richness framework was used to measure the communication potential of the social media platforms for sustainability communication.

The results indicated that the extent of sustainability posts on social media represented less than 20% of total social media posts. The nature of posts by the Top 50 ASX companies was higher on social issues than on environmental issues, which is contradictory to many previous studies. The study also found that while the social media platforms afforded high levels of media richness, most companies failed to exploit the platforms’ full potential to disseminate sustainability information.

This work provides both empirical and theoretical contributions to the ongoing debate concerning the use of social media for sustainability communication. The paper extends Lodhia et al.’s (2020) study of social media use for legitimation purposes and adapts Lodhia’s (2004) media richness framework to social media for sustainability reporting. It adds empirical insights into social media’s communication potential and value for communicating sustainability information.

The extent and nature to which organisations use social media to disclose their sustainability performance has significant practical implications for a variety of stakeholders. The results reveal to these stakeholders and the companies themselves the level of utilisation of social media along with the potential that can be harnessed. These results can potentially improve the quantity, timeliness and usability of sustainability reporting using social media platforms.

The study provides valuable evidence to increase understanding of the sustainability social media communication landscape, which organisations can potentially leverage to communicate their messages. Additionally, sustainability awareness is increased across various demographics by disseminating sustainability information to the wider public. This study will assist policy-setters in developing guidance for using social media for sustainability reporting.

This study extends existing literature, particularly the Lodhia et al. (2020) study, which has primarily focused on examining sustainability content in the media with limited exploration of the communication potential of social media platforms to communicate sustainability content.

In modern societies, consumer well-being is dependent on choices regarding complex services, such as investments, health care, insurance and lending. However, evaluating costs of such services is often difficult for consumers due to a combination of limited cognitive resources and complexity of the service. The purpose of this study is to empirically examine to what extent three specific consequences of complexity influence consumer tendencies to make mistakes when evaluating the costs (or price) of complex services.

Three studies were conducted (survey: n = 153, experiment: n = 332 and conjoint analysis: n = 225), all focusing on how consumers evaluate costs in the complex mutual fund setting.

The authors find that consumers struggle with estimating and using cost information in decision-making in the complex services setting. Consumers of complex services frequently underestimate the costs over the long-term, may see costs as a signal of service quality and are susceptible to influence from presentation formats when evaluating costs.

The study investigates mutual funds, which is one example of a complex service. In order to get a full picture of how consumers deal with costs in complex setting, future research needs to expand this focus to other types of complex services.

The results have implications for both marketers of complex services and policymakers. For marketers, this paper highlights that competing with a low-cost strategy may be difficult in the complex services setting as consumers may lack the ability to actually evaluate what they pay over the long term. For policymakers, increased simplification of prices may be an attractive option. However, it is important that this simplification is done in a way that increases the possibility to compare prices.

As complexity influences several aspects of decision-making, an understanding of how consumers evaluate costs in complex settings is dependent on taking a multidimensional research approach. This paper makes a novel contribution to the literature on pricing by showing that consumers struggle with multiple aspects when evaluating costs in complex contexts. Understanding these effects is important to policy, as well as to research on the cognitive value of simplicity that is currently gaining traction in marketing research.

The purpose of this paper is to elucidate the moral dimensions of accounting by examining the case of Gross National Happiness (GNH) in Bhutan, and to propose a new approach to accounting that is grounded in the Buddhist principle of the Middle Path. This approach aims to promote well-being and happiness, contrasting with traditional accounting practices.

The paper outlines the core concepts of the Middle Path theory and GNH. The authors first problematise the role of traditional accounting in the well-being and happiness project. The authors explore accountability from the Middle Path perspective, which is a key aspect of Buddhist philosophy. Using the concept of Middle Path accountability and GNH in practice, the authors then examine accounting in terms of the four “immeasurable moral virtues” (tshad med bzhi) of the Middle Path. The authors conclude by highlighting the value of the Middle Path for conceptualising accountability and emancipating contemporary accounting from its ethical and theoretical constraints.

This paper compares the application of traditional accounting and accountability with the Middle Path and GNH practices. The authors find that ethical discourses in traditional accounting and accountability are not compatible with the values of the Middle Path, thereby limiting the scope of accounting and accountability. This constraint is overcome by introducing four “immeasurable moral virtues” (tshad med bzhi) of Buddhism, which promote spiritual development (wisdom) to replace the existing ethical strands of traditional accounting and accountability to support the well-being and happiness project.

The study is limited to the review of concepts in GNH and Buddhist philosophy. More empirical studies in different contextual settings could increase understanding of how the practice of Middle Path and GNH could drive the project of well-being and happiness through accounting.

The paper seeks to contribute to the operationalisation of GNH in organisation by framing social and well-being accounting grounded in the Middle Path theory. The authors also seek to clarify the role of accounting as a social and moral practice.

Situated within the fields of social and moral accounting, the paper seeks to elevate the potential role of accounting in the promotion of well-being and happiness of people and other sentient beings. By applying four moral virtues of love, compassion, appreciative joy and equanimity in accounting, the authors seek to enhance the role of accounting that could potentially reduce poverty, social inequity, corruption and promote harmony and cultural well-being.

This study undertakes a conceptual integration of the GNH and Middle Path philosophy to understand the theoretical and ethical implications of traditional accounting and accountability. This contribution to the literature expands the possibilities of accounting and accountability on social and well-being accounting by introducing the Middle Path and GNH concepts.

Climate change has been universally recognized as a major threat to human well-being, necessitating a comprehensive transformation of people's activities. Various measures have been proposed to contain climate change among which the green transformation grabs special attention, thanks to its desirable properties. Within the green transformation process, green tourism comes to prominence with huge potential. As one of the largest carbon emitters, the transition towards green tourism may offer substantial benefits not only for tourism companies but also for the whole economy. Yet, most studies tend to focus on the adverse effects of tourism on climate change while overlooking the potential impact of climate change on tourism. This chapter clarifies the feedback relationship between climate change and tourism and makes some recommendations.

In the context of the journey toward digital transformation and the realization of a fully connected factory, concepts such as data science, artificial intelligence (AI), machine learning (ML) and even predictive models emerge as indispensable pillars. Given the relevance of these topics, the present study focused on the analysis of customer complaint data, employing ML techniques to anticipate complaint accountability. The primary objective was to enhance data accessibility, harnessing the potential of ML models to optimize the complaint handling process and thereby positively contribute to data-driven decision-making. This approach aimed not only to reduce the number of units to be analyzed and customer response time but also to underscore the pressing need for a paradigm shift in quality management. The application of AI techniques sought to enhance not only the efficiency of the complaint handling process and data accessibility but also to demonstrate how the integration of these innovative approaches could profoundly transform the way quality is conceived and managed within organizations.

To conduct this study, real customer complaint data from an automotive company was utilized. Our main objective was to highlight the importance of artificial intelligence (AI) techniques in the context of quality. To achieve this, we adopted a methodology consisting of 10 distinct phases: business analysis and understanding; project plan definition; sample definition; data exploration; data processing and pre-processing; feature selection; acquisition of predictive models; evaluation of the models; presentation of the results; and implementation. This methodology was adapted from data mining methodologies referenced in the literature, taking into account the specific reality of the company under study. This ensured that the obtained results were applicable and replicable across different fields, thereby strengthening the relevance and generalizability of our research findings.

The achieved results not only demonstrated the ability of ML models to predict complaint accountability with an accuracy of 64%, but also underscored the significance of the adopted approach within the context of Quality 4.0 (Q4.0). This study served as a proof of concept in complaint analysis, enabling process automation and the development of a guide applicable across various areas of the company. The successful integration of AI techniques and Q4.0 principles highlighted the pressing need to apply concepts of digitization and artificial intelligence in quality management. Furthermore, it emphasized the critical importance of data, its organization, analysis and availability in driving digital transformation and enhancing operational efficiency across all company domains. In summary, this work not only showcased the advancements achieved through ML application but also emphasized the pivotal role of data and digitization in the ongoing evolution of Quality 4.0.

This study presents a significant contribution by exploring complaint data within the organization, an area lacking investigation in real-world contexts, particularly focusing on practical applications. The development of standardized processes for data handling and the application of predictions for classification models not only demonstrated the viability of this approach but also provided a valuable proof of concept for the company. Most importantly, this work was designed to be replicable in other areas of the factory, serving as a fundamental basis for the company’s data scientists. Until then, limited data access and lack of automation in its treatment and analysis represented significant challenges. In the context of Quality 4.0, this study highlights not only the immediate advantages for decision-making and predicting complaint outcomes but also the long-term benefits, including clearer and standardized processes, data-driven decision-making and improved analysis time. Thus, this study not only underscores the importance of data and the application of AI techniques in the era of quality but also fills a knowledge gap by providing an innovative and replicable approach to complaint analysis within the organization. In terms of originality, this article stands out for addressing an underexplored area and providing a tangible and applicable solution for the company, highlighting the intrinsic value of aligning quality with AI and digitization.