Search results

The purpose of this research study is to improve sentiment analysis (SA) at the aspect level, which is accomplished through two independent goals of aspect term and opinion extraction and subsequent sentiment classification.

The proposed architecture uses neighborhood and dependency tree-based relations for target opinion extraction, a domain–ontology-based knowledge management system for aspect term extraction, and deep learning techniques for classification.

The authors use different deep learning architectures to test the proposed approach of both review and aspect levels. It is reported that Vanilla recurrent neural network has an accuracy of 83.22%, long short-term memory (LSTM) is 89.87% accurate, Bi-LSTM is 91.57% accurate, gated recurrent unit is 65.57% accurate and convolutional neural network is 82.33% accurate. For the aspect level analysis, ρ_aspect comes out to be 0.712 and Δ²_aspect is 0.384, indicating a marked improvement over previously reported results.

This study suggests a novel method for aspect-based SA that makes use of deep learning and domain ontologies. The use of domain ontologies allows for enhanced aspect identification, and the use of deep learning algorithms enhances the accuracy of the SA task.

The primary aim of this research is to conduct a comprehensive investigation into the essential elements of Industry 4.0 implementation within Indian Micro, Small and Medium Enterprises (MSMEs). Acknowledging the MSME sector as a crucial contributor to the Indian economy and industrial development, the study delves into the assessment of MSMEs based on Industry 4.0 components. Additionally, it explores the profound impact of these components on various performance factors, including organizational performance, sustainability performance and human-related aspects. The paper further ranks these identified components based on their significance within the MSME sector.

Employing a combination of methodological approaches, the research utilizes the Best and Worst Method (BWM), Data Envelopment Analysis (DEA) and calculates the Maturity Index for Industry 4.0 components. The BWM, a recognized multi-criteria decision-making technique, is initially applied to determine the weights and rankings of the identified components. Furthermore, the study evaluates 30 MSMEs, spanning manufacturing and service sectors, through the DEA approach. Industry 4.0 components are treated as inputs, and performance factors serve as outputs. Data for the analysis are collected through questionnaires distributed to the selected MSMEs. Lastly, the Maturity Index for MSMEs is also calculated.

From the result of the BWM method “assistive manufacturing” was found to be a highly weighted key component of Industry 4.0. From the DEA analysis out of 30 MSMEs 13 SMEs were highlighted as being efficient, whereas 17 MSMEs were judged to be inefficient. Furthermore, from the maturity index calculation, overall Maturity Index was determined to be 3.33 which shows that Industry 4.0 is in its initial stage of implementation, but it has gained pace in its implementation.

The research contributes to practical implications by offering a more accurate assessment of the state of Industry 4.0 implementation within MSMEs. The introduced maturity index proves instrumental in pinpointing key components that have received inadequate attention. This information is crucial for MSME managers and policymakers, guiding them in allocating resources effectively, addressing areas requiring attention and facilitating progress in the implementation of Industry 4.0. The study serves as a valuable tool for MSMEs to enhance their overall operational efficiency.

The research’s originality lies in its application of a comprehensive approach, combining BWM, DEA and the introduction of a maturity index for Industry 4.0 components in the MSME context. By employing these methodologies, the study not only identifies influential components but also provides a nuanced understanding of their relative significance. The research contributes significantly to the broader understanding of Industry 4.0 adoption, particularly, in the vital MSME sector within the Indian context. The findings are valuable for researchers, practitioners and policymakers seeking insights into improving the efficiency and effectiveness of MSMEs in the era of Industry 4.0.

The fertilizer industry (FI) is well known for its high energy needs, reliance on limited natural resources, and negative environmental impacts (EIs). The consumption of 14.2 billion tons (BT) of materials and the extraction of 1,580 tons of resources per acre are solely attributed to the FI. Because of FI's resource and energy-intensive nature, it becomes crucial for FI to adopt a Circular Economy (CE) to improve efficiency, energy, and resource reuse. However, FI needs to strengthen its progress toward CE adoption. The proposed study comprehends and examines the barriers that inhibit the adoption of CE in FI.

A total of 15 barriers obstructing the CE in FI are identified and categorized into seven different categories. The barriers were identified by performing a comprehensive literature review and expert input. The study employs the DEMATEL approach to analyze the barriers and establish a causal relationship between them.

The study reveals that the most significant challenge to implementing CE in FI is governmental restrictions, which are followed by a lack of awareness and understanding and a need for a steady supply of bulk materials. The results comprehensively comprehend the pivotal factors that jeopardize the CE in FI and furnish a robust foundation for the methodology and tactics to surmount the barriers to CE adoption.

The literature review encompasses the barriers to the transition to CE and offers management and policy perspectives that help the FI's policy and decision-makers surmount these barriers with future research endeavors.

Supplier selection (SS) is one of the prime competencies in a sourcing decision. Taking into account the key role played by suppliers in facilitating the implementation of green supply chain management (GSCM), it is somewhat surprising that very little research attention has been imparted to the development of a strategic sourcing model for GSCM. This research aims to develop a strategic sourcing framework in which supplier organizations are prioritized and ranked based on their GSCM performance. Accordingly, the benchmark organization is identified and its strategy is explored for GSCM performance improvement.

The research develops an innovative GSCM performance evaluation framework using six parameters, namely, investment in corporate social responsibility, investment in research and development, utilization of renewable energy, total energy consumption, total carbon-di-oxide emissions and total waste generation. An integrated multicriteria decision-making (MCDM) approach is proposed in which the entropy method calculates criteria weights. The Complex Proportional Assessment (COPRAS) and the Grey relational analysis (GRA) methods are used to rank supplier organizations based on their performance scores. A real-world case of green supplier selection (GSS) is considered in which five leading India-based automobile manufacturing organizations (Supplier 1, Supplier 2, Supplier 3, Supplier 4 and Supplier 5) are selected. Surveys with industry experts at the strategic, tactical, and operational levels are carried out to collect relevant data.

The results reveal that total carbon dioxide emission is the most influential parameter, as it gains the highest weight. On the contrary, investment in research and development, and total waste generation have no significant impact on GSCM performance. Results show that Supplier 5 secures the top rank. Hence, it is the benchmark organization.

The proposed methodology offers an easy and comprehensive approach to sourcing decisions in the field of GSCM. The entropy weight-based COPRAS and GRA methods offer an error-free channel of decision-making and can be proficiently used to outrank various industrial sectors based on their GSCM performances. This research is specific to the automobile manufacturing supply chain. Therefore, research outcomes may vary across supply chains with distinct characteristics.

The basic propositions of this research are based on a real-world case. Hence, the research findings are practically feasible. The less significant parameters identified in this study would enable managers to impart more attention to vulnerable areas for improvement. This research may help policymakers identify the influential parameters for effective GSCM implementation. As this research considers all aspects of sustainability, the strategies of the benchmark supplier have a direct impact on organizations' overall sustainability. The study would enable practitioners to make various strategies for GSCM performance improvement and to develop a cleaner production system.

The originality of this research lies in the consideration of both economic, social, environmental and operational aspects of sustainability for assessing the GSCM performance of supplier organizations. Quantitative criteria are considered so that vagueness can be removed from the decision. The use of an integrated grey-based approach for developing a strategic sourcing model is another unique feature of this study.

This research work aims to arise from developing new communication channels for customer service in micro, small and medium enterprises (MSMEs), such as chatbots. In particular, the results of the usability testing of three chatbots implemented in MSMEs are presented.

The methodology employed includes participants, chatbot development platform, research methodology, software development methodology and usability test to contextualize the study's results.

Based on the results obtained from the System Usability Scale (SUS) and considering the accuracy of the chatbot's responses, it is concluded that the level of satisfaction in using chatbots is high; therefore, if the chatbot is well integrated with the communication systems/channels of the MSMEs, the client receives an excellent, fast and efficient service.

The paper analyzes chatbots for customer service and presents the usability testing results of three chatbots implemented in MSMEs.

The prime objective of this paper is to design a green supply chain management (GSCM) framework to evaluate the performance of environmental-conscious suppliers using multi-criteria decision-making (MCDM) approach.

The literature survey reveals critical factors for implementing GSCM, adopted methodologies and the result obtained by several researchers. Data have been collected by conducting surveys and interviews with strategic-level personnel of five esteemed organizations in automobile manufacturing sectors. A GSCM framework is developed in which a mathematical tool entropy–the technique for order of preference by similarity to ideal solution (TOPSIS) has been used to analyze the six parameters of automobile manufacturing unit. Initially, entropy is used to find the weights of each of the parameters that influence the decision matrix of the TOPSIS method. Secondly, the proposed GSCM framework ranks the supplier. Finally, sensitivity analysis of the model satisfies the GSCM framework and benchmarked the supplier.

The result shows that “Total CO₂ emission” has an influential role for GSCM sustainability, and hence, firms should put more effort to reduce emissions to improve overall performance. Again, the parameters like investment in R&D and total waste generation may be ignored in the selection process. The result reveals the benchmarked supplier and its strategies for effective sourcing, which would have an indirect effect on organizations' overall sustainability.

This research entirely focuses on sustainability within supply chain considering economic, social and environmental paradigms. The mathematical modeling of the proposed work considers many influential parameters and provides an easy and comprehensive decision-making technique.

The methods may be adopted by the industries for sustainable supply chain management. This study benchmarks the supplier organizations and explores the adopted policies by benchmarked organizations. Other organizations should follow the policies followed by benchmarked organization for enhancing environmental, social and economic performance. Organizations striving for sustainable development can adopt this framework for evaluation of supplier performance and benchmark with better accuracy.

The design of the GSCM framework explores both the qualitative and quantitative data based on environmental, social and economic parameters simultaneously in the evaluation of environmentally conscious suppliers. The research also investigates the constraints of the system to implement the GSCM in automobile manufacturing unit. Additionally, the sensitivity analysis justifies the benchmarked supplier and the adopted strategies to be followed by other manufacturing unit.

Information technology can play a strategic role at micro as well as at macro — organizational and national‐levels. Developed countries have extensively benefitted from this technology at both levels. Can developing countries duplicate this experience with the technology and thereby foster healthy economic environment within their boundaries and strengthen their abilities to compete in the global markets? This paper addresses this issue. The paper evaluates the prevalent applications of information technology in developing countries, deliberates the potential of the technology, and presents a framework for realizing this potential. The framework proposes strategies to assure smooth and accelerated diffusion of technology in organizations. Importantly, the framework points out the factors, unique to developing countries, that must be addressed in technology planning and implementation. Ignoring these factors may result in failed systems and continued technological disadvantage.

Agile project management methods are on the rise compared to linear approaches. The demand for the demonstrable resilience of enterprise processes is likewise strongly increasing in many domains. This paper explores the potential contribution of agility within the domain of agile project management to the resilience of the operating model of an organization.

The article builds upon case studies and semi-structured interviews at selected larger Danish enterprises.

Responding to disruptions favors adaptive and flexible approaches, which are more achievable with agile methods. By exploring the patterns of agility and resilience throughout case studies, the authors derive at a 7-step approach for considering the potentials of agility to ensure the resilience of the operating model from the top level of leadership to the foundational level of technology.

This article seeks to contribute to a more profound understanding of the impact, potential and actionability of agile project management in the light of operational resilience.

It is demonstrated that agile methods are attractive for ensuring the constitutive elements of the resilience of the operating model in terms of conscious contingencies and choices involving (rapid) changes.

During the COVID-19 period, agility has been a key instrument in ensuring business survival, e.g. by switching markets, products or sales channels.

Agility has the potential to build a strategic dimension of resilience, a synergistic relationship, which is linked to the responsiveness of an organization to change promptly, with a view toward renewal and transformation.

The aim of this case study is to exemplify the application of a change story to facilitate the user centered introduction of an AI-based assistance system. Thereby, user expectations considered critical for technology acceptance and continuance intention are actively taken into account.

Semi-structured interviews are conducted with future users of the AI-based assistance system. Data are analysed by means of inductive and deductive qualitative content analysis. The resulting categories are considered as communicational core messages and included in the developed change story.

Paradox user expectations were revealed and answered in the change story by informational and motivational means. Thus, accurate expectation management is enabled and, additionally, the users are prepared for the upcoming change process, i.e., the implementation of the AI-based assistance system.

The added value lies in the psychological handling of expectation management in addition to technical aspects, which are usually primarily focused but are not sufficient to guarantee a successfully continued use of human-AI-systems.

Sustainability trends have changed the modus operandi in businesses even as the market environment becomes more socially conscious. However, relatively little research has been conducted on integrating social sustainability aspects with a focus on corporate social responsibility (CSR) into the selection of suppliers in the service sector, particularly the banking industry. In this paper, this study aims to propose a CSR decision support methodology to evaluate and prioritize socially responsible suppliers.

A novel integrated decision support methodology composed of Shannon Entropy and TOmada de Decisão Interativa e Multicritério (TODIM) methods is introduced. The Shannon-Entropy approach is used to estimate CSR factor weights, and TODIM is used to rank the suppliers, with the process completed in a group decision setting.

A Nigerian bank was used as a case study to test and show the usefulness of the CSR-based decision framework in evaluating and selecting socially responsible suppliers. The results show the topmost ranked suppliers that are recommended for future negotiations by the case (bank). The study will enable banks to select socially responsible suppliers, which could accelerate the attainment of sustainability objectives, protect their reputations and improve competitiveness.

This study pioneers the application of a novel decision methodology based on Shannon Entropy and TODIM in selecting socially sustainable suppliers in the Banking sector of an African emerging economy-Nigeria.