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The purpose of this paper is to investigate the relationship between capabilities and renewal of organizational and business know-how in franchise networks.

This work uses a comparative case study of adaptive, absorptive and innovative capabilities to investigate knowledge renewal in 16 franchise networks.

The findings show that adaptive and innovative capabilities complement each other to foster know-how renewal. Furthermore, networks without internal R&D need to mobilize adaptive, absorptive and innovative capabilities to renew both organizational and business know-how. The findings also highlight that the three capabilities are interconnected.

The results of this research could provide insights for franchise networks to regenerate their knowledge base and ensure their long-term survival.

The underlying capabilities that explain organizational and business know-how renewal in franchises have not been investigated.

This paper aims to study antiques enthusiasts’ perspectives on the recent stagnancy in the antiques market, along with their suggestions on how the antiques trade can forge a more secure path forward.

Using a qualitative analysis approach through netnography, this paper examines archived comments of antiques enthusiasts on a “r/antiques” subreddit.

This research studies connoisseurs’ insights into the reduction in antiques sales experienced by a wide cross-section of sellers, particularly independent and small business retailers. Specifically, the results of this paper’s discourse analysis show that technological advances on one hand and socioeconomic factors (e.g. income, family structure and lifestyle) on the other hand have had a significant negative impact on demand for antiques. In addition, specific attributes such as authenticity and sustainability emerged as potential key marketing elements for invigorating the broader public’s interest in purchasing antiques.

Despite their significant insights into the antiques market, antiques enthusiasts have not received the academic attention they deserve. Through discourse analysis of comments in an online antiques community, this paper draws attention to the vulnerabilities of antiques markets to a protracted climate of slow sales, while highlighting potential strategies on how to turn the tide for struggling antiques stores.

This study aims to explore whether globalization and technology are harmful to health using a global panel data set of 52 countries over the period 1990–2019.

The study focused on four continents: Africa, the Americas, Asia/Oceania and Europe. The authors used four advanced econometric methodologies, which include the standard panel fixed effect (FE), Arellano–Bover/Blundell–Bond dynamic panel, Hausman–Taylor specification and two-stage least squares (FE-2SLS)/Lewbel-2SLS approaches.

The empirical evidence highlights the significance of globalization and technology in promoting global health. The findings suggest that globalization has various impacts on global health indicators and that technology is useful in tracking, monitoring and promoting global health. In addition, the empirical evidence indicates that a truly health-centred process of globalization and technological innovation can only be realized by ensuring that the interests of countries and vulnerable populations to health risks are adequately considered in international decision-making regarding global economic integration.

The authors suggest that achieving the aspiration of global health will entail the use of globalization and information technology to extend human activities and provide equal access to global health.

The textile industry harms the environment at every stage of production, from the acquisition of raw materials to the disposal of finished products. It is very important for the textile industry to adapt to the basic policies on environmental sensitivity and sustainability to keep up with the transformation in production processes and the rapid changes occurring around the world in order to exist in global competition. Within the scope of sustainable development goals, it is of great importance to measure and evaluate indicators of all processes of the sector. This paper aims to present application of multi-criteria decision making (MCDM) methods for the assessment of sustainable development in textile industry.

The data of a multinational clothing company’s four-year sustainability performance between 2018 and 2021 were evaluated under 22 sustainability parameters determined using two new MCDM techniques, namely the combined consensus solution method and multi-attribute ideal real comparative analysis. In determining the criteria, priority key indicators were determined by taking into account the sector’s relationship with the environment, raw material consumption and social adequacy.

According to the application results of both methods, the year 2021 shows the best performance. It has been seen that the sustainability performance of the Inditex group has increased over the years and the results of the applied models support each other. It can be suggested that the proposed approach be applied to evaluate the progress in the textile sector with the relevant data on a particular company or on a macro scale.

This study makes an important contribution to the field in terms of the fact that the methods used are recent and have no application in the field of textiles. It allows the evaluation of different sustainability criteria together using a single method. It is very important to share data on sustainability indicators with customers, employees, suppliers, investors, partner organizations and society and evaluate performance. Analyzing sustainability performance on the basis of annual reports is important in terms of identifying good practices, sharing them with the community and setting an example. In addition, using scientific methods in the evaluation of the sustainability report data published by companies regularly provides significant feedback for policymakers and academics.

Indian railways (IR) is one of the largest railway networks in the world. As a part of its strategic development initiative, demand forecasting can be one of the indispensable activities, as it may provide basic inputs for planning and control of various activities such as coach production, planning new trains, coach augmentation and quota redistribution. The purpose of this study is to suggest an approach to demand forecasting for IR management.

A case study is carried out, wherein several models i.e. automated autoregressive integrated moving average (auto-ARIMA), trigonometric regressors (TBATS), Holt–Winters additive model, Holt–Winters multiplicative model, simple exponential smoothing and simple moving average methods have been tested. As per requirements of IR management, the adopted research methodology is predominantly discursive, and the passenger reservation patterns over a five-year period covering a most representative train service for the past five years have been employed. The relative error matrix and the Akaike information criterion have been used to compare the performance of various models. The Diebold–Mariano test was conducted to examine the accuracy of models.

The coach production strategy has been proposed on the most suitable auto-ARIMA model. Around 6,000 railway coaches per year have been produced in the past 3 years by IR. As per the coach production plan for the year 2023–2024, a tentative 6551 coaches of various types have been planned for production. The insights gained from this paper may facilitate need-based coach manufacturing and optimum utilization of the inventory.

This study contributes to the literature on rail ticket demand forecasting and adds value to the process of rolling stock management. The proposed model can be a comprehensive decision-making tool to plan for new train services and assess the rolling stock production requirement on any railway system. The analysis may help in making demand predictions for the busy season, and the management can make important decisions about the pricing of services.

On the one hand, this paper is to further understand the residents' differentiated power consumption behaviors and tap the residential family characteristics labels from the perspective of electricity stability. On the other hand, this paper is to address the problem of lack of causal relationship in the existing research on the association analysis of residential electricity consumption behavior and basic information data.

First, the density-based spatial clustering of applications with noise method is used to extract the typical daily load curve of residents. Second, the degree of electricity consumption stability is described from three perspectives: daily minimum load rate, daily load rate and daily load fluctuation rate, and is evaluated comprehensively using the entropy weight method. Finally, residential customer labels are constructed from sociological characteristics, residential characteristics and energy use attitudes, and the enhanced FP-growth algorithm is employed to investigate any potential links between each factor and the stability of electricity consumption.

Compared with the original FP-growth algorithm, the improved algorithm can realize the excavation of rules containing specific attribute labels, which improves the excavation efficiency. In terms of factors influencing electricity stability, characteristics such as a large number of family members, being well employed, having children in the household and newer dwelling labels may all lead to poorer electricity stability, but residents' attitudes toward energy use and dwelling type are not significantly associated with electricity stability.

This paper aims to uncover household socioeconomic traits that influence the stability of home electricity use and to shed light on the intricate connections between them. Firstly, in this article, from the perspective of electricity stability, the characteristics of the power consumption of residents' users are refined. And the authors use the entropy weight method to comprehensively evaluate the stability of electricity usage. Secondly, the labels of residential users' household characteristics are screened and organized. Finally, the improved FP-growth algorithm is used to mine the residential household characteristic labels that are strongly associated with electricity consumption stability.

1. The stability of electricity consumption is important to the stable operation of the grid.

2. An improved FP-growth algorithm is employed to explore the influencing factors.

3. The improved algorithm enables the mining of rules containing specific attribute labels.

4. Residents' attitudes toward energy use are largely unrelated to the stability of electricity use.

The stability of electricity consumption is important to the stable operation of the grid.

An improved FP-growth algorithm is employed to explore the influencing factors.

The improved algorithm enables the mining of rules containing specific attribute labels.

Residents' attitudes toward energy use are largely unrelated to the stability of electricity use.