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Aspect category-based sentiment analysis (ACSA) has been widely used in consumer preference mining and marketing strategy formulation. However, existing studies ignore the variability in features and the intrinsic correlation among diverse aspect categories in ACSA tasks. To address these problems, this paper aims to propose a novel integrated framework.

The integrated framework consists of three modules: text feature extraction and fusion, adaptive feature selection and category-aware decision fusion. First, text features from global and local views are extracted and fused to comprehensively capture the potential information in the different dimensions of the review text. Then, an adaptive feature selection strategy is devised for each aspect category to determine the optimal feature set. Finally, considering the intrinsic associations between aspect categories, a category-aware decision fusion strategy is constructed to enhance the performance of ACSA tasks.

Comparative experimental results demonstrate that the integrated framework can effectively detect aspect categories and their corresponding sentiment polarities from review texts, achieving a macroaveraged F1 score (Fmacro) of 72.38% and a weighted F1 score (F1) of 79.39%, with absolute gains of 2.93% to 27.36% and 4.35% to 20.36%, respectively, compared to the baselines.

This framework can simultaneously detect aspect categories and corresponding sentiment polarities from review texts, thereby assisting e-commerce enterprises in gaining insights into consumer preferences, prioritizing product improvements, and adjusting marketing strategies.

To achieve changing customer demands, organizations are striving hard to embrace cutting-edge technologies facilitating a high level of customization. Industry 4.0 (I4.0) implementation aids in handling big data that could help generate customized products. Lean six sigma (LSS) depends on data analysis to execute complex problems. Hence, the present study aims to empirically examine the key operational characteristics of LSS and I4.0 integration such as principles, workforce skills, critical success factors, challenges, LSS tools, I4.0 technologies and performance measures.

To stay competitive in the market and quickly respond to market demands, industries need to go ahead with digital transformation. I4.0 enables building intelligent factories by creating smart manufacturing systems comprising machines, operators and information and communication technologies through the complete value chain. This study utilizes an online survey on Operational Excellence professionals (Lean/Six Sigma), Managers/Consultants, Managing Directors/Executive Directors, Specialists/Analysts/Engineers, CEO/COO/CIO, SVP/VP/AVP, Industry 4.0 professionals and others working in the field of I4.0 and LSS. In total, 83 respondents participated in the study.

Based on the responses received, reliability, exploratory factor analysis and non-response bias analysis were carried out to understand the biasness of the responses. Further, the top five operational characteristics were reported for LSS and I4.0 integration.

One of the limitations of the study is the sample size. Since I4.0 is a new concept and its integration with LSS is not yet explored; it was difficult to achieve a large sample size.

Organizations can utilize the study findings to realize the top principles, workforce skills, critical success factors, challenges, LSS tools, I4.0 tools and performance measures with respect to LSS and I4.0 integration. Moreover, these operational characteristics will help to assess the organization's readiness before and after the implementation of this integration.

The authors' original contribution is the empirical investigation of operational characteristics responsible for I4.0 and LSS integration.

The adoption of metaverse in manufacturing holds significant benefits, but there are several barriers to its seamless integration. This paper aims to identify such barriers and prioritize them in a manner that allows industrial leaders to strategize for smooth adoption.

In this study, we applied two-stage methods, first the identification and validation of barriers through an empirical study applied to Exploratory Factor Analysis (EFA). A purposive sampling technique and snowball sampling facilitated data collection from these expert sources. Through snowball sampling, additional contacts working in the metaverse field were reached, resulting in 235 possible respondents; the survey yielded 104 completed responses. Thereafter, the best-worst method (BWM) was used to measure and rank the barriers.

The study results show that the two most critical barriers are “Lack of data security and privacy” and “Lack of integration compatibility with existing systems.” Such findings inform industry leaders of specific recommendations for structural changes, training programs, necessary technological investments and collaborative efforts to overcome these barriers.

This work adds significantly to academic discussion by prioritizing barriers towards integrating metaverse technology in manufacturing. In addition, this strategic methodology aids in critical appraisal and ranking of barriers for successful adoption. This study also identifies key barriers but acknowledges that other unexamined factors might be lurking in the background, such as virtual economy, financial risks and cross-border legal issues.

The study’s conclusions cannot be generalized to the other sectors, thus indicating the necessity of carrying out a comparative multi-sector study in the future.

To the best of the authors’ knowledge, the study on systematic prioritization of barriers to adopting metaverse technology in manufacturing is the original contribution of the authors.

This study aims to investigate how two different types of brand-to-brand dialogues – “roasting” versus “toasting” – impact consumers’ brand perceptions, particularly perceived entertainment, and influence brand attitudes.

The research design comprises four studies. The preliminary study involves Web scraping to gauge consumer perception about the two communication approaches followed by two well-known brands. Study 1 involves an online experiment to compare these communication types within each brand tested in the pilot study and examines the mediation effect of perceived entertainment. Study 2, also an online experiment, investigates the role of message neutralization, demonstrating that “roasting” can be acceptable when the humor is neutralized. Study 3 further tests the effects of neutralized “roasting” at different levels of brand familiarity and personality.

Roasting can lead to more favorable consumer perceptions than toasting. The effect can be explained by roasting’s higher level of perceived entertainment. However, this positive outcome is contingent on the successful neutralization of the aggressive humor in the “roasting” messages. When it comes to brand familiarity and personality, familiar brands benefit more from neutralized “roasting,” whereas brand personality does not have a strong influence.

The findings suggest that “roasting” can be effective when messages are neutralized, and “toasting” works best when spontaneous and genuine. It highlights how brand familiarity and personality influence consumer reactions, thus, offering strategic insights for both established and lesser-known brands. The study also prompts further research to examine other brand traits, cultural factors and behavioral dimensions in brand-to-brand dialogue, signifying the complexity and richness of this growing research area.

This study advises lesser-known brands to adopt “toasting” strategies to build a positive image, while established brands can try “roasting,” ensuring message neutrality to avoid negativity. The research emphasizes the role of brand familiarity and personality in shaping brand dialogues. Marketers must consider these to make humor strategies effective and bolster positive brand image.

This research uniquely examines message neutralization through contextual cues as a strategy brands can use to aid their sensitive dialogues with others on social media. The findings provide new insights into how brands can use different types of messages in digital communications to attract consumers and ensure positive reception, offering valuable guidance for academics and practitioners in brand-to-brand dialogue.

Optimization of FDM process parameters has emerged as a major step toward mitigating the issue of reduced mechanical strength of fused deposition modeling (FDM) products. This study aims to evaluate the impact of process parameters on the mechanical properties of 3D-printed fabric yarns of three different materials, namely, polyethylene terephthalate glycol (PETG), nylon and thermoplastic polyurethane (TPU).

2/1 Twill fabric samples with variations of extrusion temperatures and printing profiles are produced, and the individual yarns, manually separated from the fabric samples, are tested for tensile strength, modulus, strain and energy at maximum force. The tensile test results are further subjected to statistical analysis with MINITAB software to detect the standard level of influence of each parameter and evaluate their significance.

Both extrusion temperature and printing profile, along with their interaction, are significantly important parameters for the mechanical behavior of PETG. However, for nylon and TPU, the influence of a few parameters is found to be insignificant. For all variations of fabric yarns, relatively lower extrusion temperature is concluded to be preferable for better mechanical properties.

This research is new and original. It provides knowledge about how FDM process parameters influence the mechanical behavior of fabrics. It also identifies the optimum process parameters for the best performance of the final product. This article can be a foundation for further parametric analysis of 3D-printed fabric. No artificial intelligence or similar software was used in the preparation of this manuscript.

Workflow optimisation is crucial for establishing a viable acute stroke (AS) intervention programme in a large tertiary care centre. This study aims to utilise Lean Six Sigma (LSS) principles to enhance the hospital's workflow.

The Action Research methodology was used to implement the project and develop the case study. The study took place in a large tertiary care academic hospital in India. The Define-Measure-Analyse-Improve-Control approach optimised the workflow within 6 months. Lean tools such as value stream mapping (VSM), waste audits and Gemba were utilised to identify issues involving various stakeholders in the workflow. Sigma-level calculations were used to compare baseline, improvement and sustainment status. Additionally, statistical techniques were effectively employed to draw meaningful inferences.

LSS tools and techniques can be effectively utilised in large tertiary care hospitals to optimise workflow through a structured approach. Sigma ratings of the processes showed substantial improvement, resulting in a five-fold increase in clinical outcomes. Specifically, there was a 43% improvement in outcome for patients who underwent acute stroke revascularisation. However, certain sigma ratings deteriorated during the control and extended control (sustainment) phases. This indicates that ensuring the sustainability of quality control interventions in healthcare is challenging and requires continuous auditing.

The article presents a single case study deployed in a hospital in India. Thus, the generalisation of outcomes has a significant limitation. Also, the study encounters the challenge of not having a parallel control group, which is a common limitation in quality improvement studies in healthcare. Many studies in healthcare quality improvement, including this one, are limited by minimal data on long-term follow-up and the sustainability of achieved results.

This study pioneers the integration of LSS methodologies in a large Indian tertiary care hospital, specifically targeting AS intervention. It represents the first LSS case study applied in the stroke department of any hospital in India. Whilst most case studies discuss only the positive aspects, this article fills a critical gap by unearthing the challenges of applying LSS in a complex healthcare setting, offering insights into sustainable quality improvement and operational efficiency. This case study contributes to the theoretical understanding of LSS in healthcare. It showcases its real-world impact on patient outcomes and process optimisation.

Demand planning (DP) is a key element of supply chain management (SCM) and is widely regarded as an important catalyst for improving supply chain performance. Regarding the availability of technology to process large amounts of data, artificial intelligence (AI) has received increasing attention in the DP literature in recent years, but there are no reviews of studies on the application of AI in supply chain DP. Given the importance and value of this research area, we aimed to review the current body of knowledge on the application of AI in DP to improve SCM performance.

Using a systematic literature review approach, we identified 141 peer-reviewed articles and conducted content analysis to examine the body of knowledge on AI in DP in the academic literature published from 2012 to 2023.

We found that AI in DP is still in its early stages of development. The literature is dominated by modelling studies. We identified three knowledge clusters for AI in DP: AI tools and techniques, AI applications for supply chain functions and the impact of AI on digital SCM. The three knowledge domains are conceptualised in a framework to demonstrate how AI can be deployed in DP to improve SCM performance. However, challenges remain. We identify gaps in the literature that make suggestions for further research in this area.

This study makes a theoretical contribution by identifying the key elements in applying AI in DP for SCM. The proposed conceptual framework can be used to help guide further empirical research and can help companies to implement AI in DP.

The paper contends that the migrant workers experience a significant impact on their relations back in the place of origin. In this context, this paper aims to explore, in depth the migration of the labourers to Kashmir and the challenges experienced by them in terms of adaptation to an unfamiliar culture, health and well-being, stereotyping, limited availability of collective religious spaces and the impact of migration on their social relations with the kith and kin way back in the place of origin. This is a qualitative study based on 20 oral narratives with respondents who experienced significant socio-cultural challenges in the host societies. The evidence-based findings reveal that significant challenges are experienced by the migrant workers in Kashmir, with respect to the social and cultural aspects of their lives.

Keeping in view the nature and the objectives of the study, qualitative research approach was used to have an empathetic understanding of the experience of obstacles by the migrant workers pertaining to cultural adaptation, health and well-being, stereotyping and other hard to adjust problems. The study used a convenience sampling technique to recruit the respondents in the district Pulwama of the union territory of Jammu and Kashmir. In-depth face-to-face interviews were conducted with 12 daily wage migrant labourers who after migrating to the valley of Kashmir, experienced challenges with respect to the cultural adaptation, health and well-being, stereotyping and employment opportunities. The data was collected in the month of June and July, keeping in view the massive influx of migrant worker in the valley. While collecting data from the first ten participants, the study encountered data saturation, as the same set of responses were observed. Meanwhile, two more respondents were added to the study to ensure that no data is left out, but again the same set of response was recorded. This prompted the investigator to cease the data collection process. Moreover, the current study was conducted at a time and place convenient to participants, usually at the online e-service shops, where these daily wage migrant labourers come for money transfer and other related activities. Thematic analysis of data was practiced as per the technique of Braun and Clarke (2006), which involves becoming acquainted with the data, looking for themes, validating and refining those themes, identifying and labelling those themes and finally creating the report.

The evidence-based findings reveal that significant challenges are experienced by the migrant workers in Kashmir, with respect to the social and cultural aspects of their lives.

Original research paper.

This paper aims to analyze the dynamic linkages of the energy market with the forex market. The energy market is measured by crude oil WTI, while the forex market is proxied by Brazilian real (RBRL), Mexican peso (RMXN), South African rand (RZAR), Turkish lira (RTRY) and British pound sterling (RGBP) exchange rate.

For the study, daily observations of these constituent asset classes extending from December 31, 2019, to August 16, 2022, are taken as the data. Furthermore, it is categorized into two different sub-samples in the form of the COVID-19 outbreak (December 31, 2019 to February 23, 2022) and the Russo−Ukraine invasion (February 24, 2022 to August 16, 2022). For empirical estimation, Diebold and Yilmaz model (2014) and Barunik and Krehlik test (2018) are used to examine the dynamic linkages.

The study concludes that the Mexican peso (RMXN) receives and transmits the highest spillover, while crude oil (RCOWTI) receives and transmits the least volatility to the network connection in full sample. In addition, the authors report that the dynamic linkage is not constant in the short, medium and long run. Furthermore, the spillover index in the Russo−Ukraine invasion is higher (29.92%) than full observation (22.03%) and COVID-19 outbreak (21.10%) in the short run.

This paper ventures to offer insight to investors, traders and policymakers based on normal trading days and crisis periods.

In the current scenario, with rapid socioeconomic transformation occurring in all aspects of society, the adversities experienced by older adults have come to the forefront. The lived experiences within joint families are making it harder for the elderly to adjust to the changed familial setup. In such a situation, they undergo many problems which are most often overlooked and considered not worthy of serious attention. Loneliness and isolation are the most crucial and serious issues experienced by the elderly within the nuclear family. After spending a significantly larger part of their lives within large families, with caring people, interdependent, adjusting to the changing familial setup has become hard and strenuous. Moreover, lack of emotional support is yet another major concern of the elderly. As they grow older, care and emotional support become necessary to maintain an effective and active life in old age. However, such emotional support and care that the joint family offered is missing in the changing familial setup. Now, in light of these issues, this paper aims to explore the multidimensional concerns of the elderly (60 years and above) who, on account of familial transitioning from joint to nuclear in Kashmir, are experiencing isolation and other, hard to adjust, life changes. The paper is based on 20 oral narratives conducted, over time, with elderly people undergoing life changes due to family transition from joint to nuclear. The finding of the study revealed that the elderly experience a greater degree of change in their lifestyle due to the family transition from joint to nuclear.

This is a qualitative study that describes the multidimensional concerns of the older adults. The study was conducted in the union territory of Jammu and Kashmir using in-depth face-to-face interviews with 20 respondents aged 60 years and above who have witnessed a transition in family from joint to nuclear and have undergone many problems during this transition. Apart from this, two interviews with the sons of older persons were also included in the study to know the status of age in the current situation. After the data was collected from the first 10 respondents, the study witnessed a data saturation, as the same set of results were obtained. Meanwhile, 3 more respondents were included in the study to make sure that no data was left behind; however, again same results were witnessed. This led the investigator to halt the data collection process. Thereafter, the data was then analyzed using Braun and Clarke's (2006) thematic analysis technique, which involved familiarizing oneself with the data, looking for themes, validating and refining themes, identifying and labeling themes and finally creating the report.

The traditional joint family is undergoing rapid transformation in the current scenario. With a trend toward the nuclearization of families, which resulted in the diminishing status of the elderly, the vulnerabilities of the elderly have come to the forefront (Kumar et al., 2014). The transition of the family from joint to nuclear can be difficult for the elderly as it involves the elderly having to adjust and adapt to a new social milieu, which can be quite complex as it involves major changes in their lifestyle and daily routine. The elderly may also experience difficulty with increased social isolation and reduced family interaction. Such problems may seem insignificant and trivial to the younger ones, but stepping into the shoes of the elderly would help us understand the intensity and vulnerability of such problems. This cultural shift is quickly making changes, in part due to the rise of individualism in modern societies and the existence of a consumerist perspective among the younger generations. Such situations lead to greater alienation and isolation among the elderly from society in general and the members of the family in particular.

The is an empirical study conducted to know the status of older adults in nuclear families.