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Given the lack of focus on a standardized measurement framework (e.g. benchmarking tool) to assess and quantify complexity within the supply chain, this study has developed a unified supply chain complexity (SCC) index and validated its utility by examining the relationship with firm performance. More importantly, it examines the role of firm owners' business knowledge, sales strategy and board management on the relationship between SCC and firm performance.

In this study, the unit of analysis is Indian manufacturing companies listed on the Bombay Stock Exchange (BSE). This research has merged panel data from two secondary data sources: Bloomberg and Prowess and empirically operationalized five key SCC drivers, namely, number of suppliers, the number of supplier countries, the number of products, the number of plants and the number of customers. The study employs panel data regression analyses to examine the proposed conceptual model and associated hypotheses. Moreover, the present study employs models that incorporate robust standard errors to account for heteroscedasticity.

The results show that complexity has a negative and significant effect on firm performance. Further, the study reveals that an owner's business knowledge and the firm's effective sales strategy and board management can significantly lessen the negative effect of SCC.

This study develops an SCC index and validates its utility. Also, it presents a novel idea to operationalize the measure for SCC characteristics using secondary databases like Prowess and Bloomberg.

The purpose of this work is to study the relationship between the fabric’s mechanical properties such as tear strength (T_S), breaking strength (B_S) and cloth’s dimensional stability (Sh), particularly, after industrial launderings (stone wash, enzyme wash, mixed wash and rinse). Hence, we select the most interrelationships using the principal component analysis (PCA) technique. In this study, the treatments of finishing garments during washing are the important parameters influencing the cloth’s dimensional and the fabric’s mechanical properties. To improve the obtained results, the selected significant inputs are also analyzed within their influence on shrinkage. The polynomial regression model relating the tear strength and the shrinkage of denim fabric proves the effectiveness of the PCA method and the obtained findings.

To investigate the matter, the type of washing, and their contributions to shrinkage, four types of fabrics manufactured into pants were used. These fabrics differ not only by their basis weights (medium and heavy weight fabrics) but, also by their compositions (within and without elastane) and their thread count (warp and weft yarn count, twist and density. To evaluate significant results, a factorial design analysis based on an experimental design was established. The choice of these treatments, as well as their design mode, led us to make a complete factorial experimental design.

According to the results, the prediction of shrinkage behavior as a function of the process washing input parameters seems significant and useful in our experimental design of interest. As a consequence, it was also concluded that after these input parameters, we can find the relationship between the shrinkage (Sh_warp and Sh_weft) and the mechanical properties such as tear strength (T_Swarp and T_Sweft) and breaking strength (B_Swarp and B_Sweft). Thanks to the PCA, it is very easy to reduce the number of the influent output parameters, and knowing these significant parameters, the prediction of mechanical properties knowing the shrinkage of denim garment, during the process of washing seems successful and can undoubtedly help industrial to minimize the poor workmanship of the finishing quality.

This study is very interesting for finishing denim garments. The shrinkage is very important for correcting measures in sewing, considering that a high shrinkage may cause the cancellation of the fit from the client. This type of defect cannot be repaired in the major part of the cases and causes a big loss for the company, moreover the mechanical properties. For this reason, analyzing the value of shrinkage before starting the production cycle is of great importance to apply the right balance to the pattern. The model of predicting the mechanical properties behaviors as a function of the shrinkage denim garment leads manufacturers to eliminate the test of mechanical properties that remain as destructive tests. Moreover, according to the results obtained, it may be concluded that prediction is still accurate through the shrinkage test which is an inevitable test. Even though, these results can bring a huge gain for the garment wash industries.

This work presents the first study predicting a relationship between the mechanical properties and denim garment shrinkage, applying the PCA technique to minimize the all-output parameters that are not significant or correlated with each other. Besides, it deals with the relationship developed between the fabric’s mechanical properties such as tear strength (T_S), breaking strength (B_S) and cloth’s dimensional stability (Sh), particularly, after industrial launderings (stone wash, enzyme wash, mixed wash and rinse). Moreover, it is notable to mention that the originality of this study is to let to the garment wash industries to save in production time of orders and also in quality.

Fake news refers to intentionally fabricated or misleading information designed to deceive the public and manipulate opinions for personal, political or financial gain. Most existing detection methods primarily focus on capturing language features from news content. However, these methods neglect the varying importance of different news entities. Additionally, these methods tend to overlook the auxiliary role of external knowledge, resulting in an incomplete understanding of the entity. To address these issues, this paper aims to propose a Dual-layer Semantic Information Extraction Network with External Knowledge (DSEN-EK) for fake news detection.

This approach is proposed to comprise three parts: Dual-layer Semantic Information Extraction Network, Entity Integration Network with External Knowledge and Classifier. Specifically, Dual-layer Semantic Information Extraction Network is designed to enhance relationships between entities and the influence of important entity representations. The Entity Integration Network with External Knowledge is proposed to extract entity descriptions from external knowledge bases.

The DSEN-EK model performs well on the Liar, Constraint, Twitter15 and Twitter16 data sets, achieving accuracy of 98.02%, 94.61%, 90.09% and 93.65%, respectively. These results highlight its effectiveness in detecting fake news across different types of content.

The Dual-layer Semantic Information Extraction Network is proposed to capture the relationships between entities and enhance the continuous semantic information of the news. The Entity Integration Network with External Knowledge is designed to enrich entity descriptions, leading to a more comprehensive capture of semantic details.

This paper aims to investigate and compare the impact of foreign and domestic institutional investors on the market value of family and non-family companies. Subsequently, it examines how different degrees of family ownership influence foreign and domestic institutional investors and their value impacts.

The sample of this study includes 339 non-financial firms from NIFTY-500 for 11 years from 2011 to 2020, which contains 128 family and 211 non-family companies. Both static (fixed-effect model) and dynamic (two-step system generalized method of moments) models are employed to test the hypotheses.

Findings suggest that foreign institutional investors outshine domestic institutions regarding value creation. Meanwhile, higher (>50%) family holdings are detrimental to foreign institutional investors, while moderate holdings (26–49%) improve domestic institutional investments. The favorable effect of foreign players gets diluted with the higher (>50%) family holdings, while the adverse impact of domestic players improves with the moderate (26–49%) family holdings. Overall, partial family control is beneficial, while low and absolute family control is detrimental to market value. These findings indicate that institutional investors are family control-dependent, where the family control effect is not static.

This paper offers a novel perspective by addressing the effect of costs and benefits realized at three distinctive levels of family holdings on foreign and domestic institutional investors and their value impacts to witness differences caused by varying family control, which is not done earlier as per the best of our knowledge.

The aim of this study is to enhance the performance of cloth recommendation systems by proposing a hybrid adversarial network called Cloth-Net, which integrates Dense Vision Transformers for effective 2D-3D image classification.

Cloth-Net combines the strengths of adversarial networks with Dense Vision Transformers to process both 2D and 3D images for improved classification. The model was trained on a large-scale dataset of clothing images, using a hybrid adversarial approach that enhances both feature extraction and image classification accuracy. The methodology also includes data augmentation and transfer learning techniques to optimize the model’s generalization capability.

Experimental results demonstrate that Cloth-Net significantly outperforms traditional convolutional neural network-based methods in terms of accuracy, precision, and recommendation quality. The hybrid adversarial framework, together with Dense Vision Transformers, enables the model to better understand complex clothing images, leading to more accurate and personalized recommendations.

This study introduces a novel hybrid adversarial model, Cloth-Net, that uniquely combines Dense Vision Transformers with traditional adversarial networks for the first time in the context of 2D-3D image classification. The findings present a substantial improvement in the performance of cloth recommendation engines, making the proposed model valuable for both academic research and practical applications in fashion technology.

This study focuses on forecasting demand for markdown pricing in situations where historical data are limited and proposes using a hybrid knowledge-based residual (KRL) network to enhance the accuracy of demand predictions in fast fashion retailing.

We use a hybrid KRL network structure to increase forecasting accuracy in fast fashion data by combining artificial neural network (ANN) and theoretical demand models (TDMs) such as linear, exponential and multinomial logit. We used a linear demand model (LDM) as a theoretical demand model, which is one of the popular TDMs in the literature, and combined it with neural networks utilizing the residual network structure. We tested it on real fast fashion data to estimate the next week’s demand at the clearance seasons of 5 years.

The results underscore KRL’s capability to derive mutual benefits from both neural networks and LDM, especially in the specific context of limited fast fashion data. Furthermore, KRL outperforms LDM and ANN models when used individually in forecasting accuracy.

The research paper proposes a scientific, quantitative method for forecasting the sales for markdown settings, combining data driven and TDMs using residual learning thereby resolving the issue of insufficient sales data in the fashion industry.

Smartphone demand has been driving people towards refurbished electronic products. However, a lack of transparency in refurbished product pricing makes purchases time-consuming and reduces customer trust. Thus, our research aims to help practitioners and researchers understand how product life and usage characteristics, technical attributes and crowdsourced product reviews and sentiments affect exchange prices for refurbished/remanufactured smartphones.

Our five-stage exchange price predictive framework begins with data gathering and predictor variable identification. Thereafter, customer review data were scraped to populate both customer ratings and textual content, enabling sentiment analysis for the various smartphone configurations. Stepwise regression was used to find statistically significant factors and validate the predictive model. Testing for nonlinear effects, normality, outliers and homoskedasticity warrants power transformation of the target variable. The analysis used data from GSMArena.com and Amazon.com.

Our study validates extant findings and provides several novel insights for functional yet hedonistic products like smartphones. Unlike other pure hedonistic products, refurbished phone buyers care more about usage duration than life. Besides having a strong affinity for the sleekness of the phone, such customers are strongly dissuaded by the presence of negative textual content in the customer reviews.

Our study augments the current understanding of exchange price modelling by bringing in perspectives from life cycle characteristics, technical attributes and product reviews.

The purpose of the paper is to examine the trade structure of India’s pharmaceutical sector with a focus on intra-industry trade (IIT).

This paper starts with analysing export destinations and import sources using significant trade shares; the study calculates IIT between India and its consistent trade partners at an aggregate level and considers the problem of categorical aggregation at a disaggregate level. To determine the determinants of IIT at different levels, the Vector Error Correction model used production-related data to identify the drivers of IIT. Also, the Granger causality test was used for short-run causality.

This study examining India’s consistent trade partners from 1993 to 2023, finds long-run association and short-run causality. The results show a significant long-run association between total IIT and factors like unskilled labour share, invested capital, fuel consumption, total input and net value added. The key low-vertical IIT (LVIIT) drivers are invested capital, unskilled labour, fixed capital and total inputs. The negative long-run association between the total input and LVIIT obtained implies a rising level of total input cost, leading to a fall in IIT and LVIIT. Also, a negative association is obtained for unskilled labour and total IIT, while a positive association is obtained for LVIIT. In the short run, causality indicates that total IIT is influenced by invested capital and fuel consumption, while unskilled labour shares and total inputs drive LVIIT. Both IIT types impact invested capital, highlighting the need for policy intervention in input markets. It provides insights for improving quality trade expansion and correcting production-related factors.

Unlike other studies on the pharmaceutical trade in India, this study analyses India’s pharmaceutical trade for a longer time period, focusing on destination-wise analysis and calculating the intra-industry trade index while taking care of the problem of categorical aggregation. Further, the study attempted to find the long-run association with production-related drivers.

This study has been carried out in dairy product manufacturing industry of Northern India to judge the significance of supply chain strategies viz. cost reduction and optimization strategies toward performance improvement and mediator effect of strength and opportunity between strategies and performance parameters.

Questionnaire survey has been performed to justify their role toward performance improvement. Structural equation modeling, descriptive statistics, hierarchical regression and clusters and partial least square-structural equation modeling has been applied for ascertain the benefits occurred.

Results indicated that strength (success factors of strength identified from literature) is the significant mediator than opportunity (success factors) for enhancing performance of organization. Small incremental improvements and 5S activities are highly important cost reduction strategies for enhancing performance of supply chain strategies. Supply chain strategies significantly improve the quality of milk in the industry under study. The customer has got high-quality product after implementing supply chain strategies. Right-time delivery of product is only possible if failure modes are analyzed thoroughly. Reliability of performance parameters is 88% which signifies that high benefits are achieved by implementing cost reduction and cost optimization strategies of supply chain concept. Processing of milk is significantly improved after taking combined effect of processing and delivery of product.

This paper helps both academics and managers to gain a better understanding of this question by considering the role of supply chain strategies implementation practically through a standard procedure.

Artificial intelligence (AI)-based human capital management (HCM) software solutions represent a potentially effective way to leverage and streamline a bank’s human resources. However, despite the attractiveness of AI-based HCM solutions to improve banks’ effectiveness, to the best of the authors’ knowledge, there are no current studies that identify critical success factors (CSFs) for adopting AI-based HCM in the banking sector. This study aims to fill this gap by investigating CSFs for adopting AI-based HCM software solutions in the banking sector.

Full consistency method methodology and technology–organization–environment, economic and human framework are used for categorizing and ranking CSFs.

The study identifies the technological and environmental dimensions as the most and least important dimensions for AI-based HCM adoption in banks. Among specific CSFs, compatible technology facilities, sufficient privacy and security and relative advantages of technology over competing technologies were identified as the most important. Implementation of AI-based HCM solutions requires significant outlays of resources, both human and financial, for banks.

The study provides bank administrators a set of objective parameters and criterion to evaluate the feasibility of adopting a particular AI-based HCM solution in banks.