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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.

Until now, it is not clear whether there are differences in patient perception between multi-bedded rooms with two and four beds. The purpose of this study was to investigate the effect of the physical (i.e. room type) and psychosocial (i.e. kindness of roommates and extraversion) aspects on the patients’ experience (i.e. pleasantness of the room, anxiety, sleep quality) in multi-bedded rooms in an oncology ward.

A group of 84 hospitalized oncology patients completed a questionnaire on the day of departure. Room types were categorized into two groups: two-person and four-person rooms.

Multivariate logistic regression analyses with the minimum Akaike Information Criterion (AIC) showed no direct main effects of room type (two vs. four-person room), kindness of roommates and extraversion on pleasantness of the room, anxiety and sleep quality. However, the authors found an interaction effect between room type and extraversion on pleasantness of the room. Patients who score relatively high in extraversion rated the room as more pleasant when they stayed in a four-person rather than a two-person room. For patients relatively low in extraversion, room type was not related to pleasantness of the room.

The findings allow hospitals to better understand individual differences in patient experiences. Hospitals should inform patients about the benefits of the different room types and potential influences of personality (extraversion) so patients are empowered and can benefit from autonomy and the most appropriate place.

This study emphasizes the importance of including four-person rooms in an oncology ward, while new hospital facility layouts mainly include single-bed rooms.

The purpose of this study is to empirically examine the impact of cosmopolitan consumer orientation (CCO) on sustainable apparel consumer behavior.

A total of 469 US responses collected using MTurk were retained for the analysis after screening for unengaged responses. Structural equation modeling was used to confirm the factor structure of the measurement model and to analyze the structural model. A two-step cluster analysis using log-likelihood distance measure and Akaike's Information Criterion was conducted to explore consumer profiles and past behavior.

Based on the model results, CCO positively impacts apparel sustainability knowledge, attitude toward purchasing sustainable apparel, perceived norm and sustainable apparel purchase intention. Attitude and perceived norm also impact sustainable purchase intention. The two-step cluster analysis, based mainly on sustainable past behavior, reveals that the group of sustainability engaged consumers knows more about apparel sustainability, has a stronger intention to purchase sustainable apparel, is more cosmopolitan and shows a higher tendency to follow social norms. Consumers in this group also tend to live in metropolitan areas and are slightly younger than unengaged consumers.

This study expands CCO research linking two major trends in society and industry: cosmopolitanism and sustainable apparel consumer behavior. The study reveals that CCO uplifts consumers' sustainable behavior and provides evidence in support of CCO as a driver of sustainable consumer behavior. Moreover, results imply a positive future outlook for the diffusion of sustainable apparel, as well as a much-needed mainstream consumer adhesion to more sustainable lifestyles. Given the repercussions of the findings, this research has numerous theoretical as well practical implications.

This study was conducted to forecast the monthly number of tuna landings between 2023 and 2030 and determine whether the estimated number meets the government’s target.

The ARIMA and seasonal ARIMA (SARIMA) models were employed for time series forecasting of tuna landings from the Malaysian Department of Fisheries. The best ARIMA (p, d, q) and SARIMA(p, d, q) (P, D, Q)¹² model for forecasting were determined based on model identification, estimation and diagnostics.

SARIMA(1, 0, 1) (1, 1, 0)¹² was found to be the best model for forecasting tuna landings in Malaysia. The result showed that the fluctuation of monthly tuna landings between 2023 and 2030, however, did not achieve the target.

This study provides preliminary ideas and insight into whether the government’s target for fish landing stocks can be met. Impactful results may guide the government in the future as it plans to improve the insufficient supply of tuna.

The outcome of this study could raise awareness among the government and industry about how to improve efficient strategies. It is to ensure the future tuna landing meets the targets, including increasing private investment, improving human capital in catch and processing, and strengthening the system and technology development in the tuna industry.

This paper is important to predict the trend of monthly tuna landing stock in the next eight years, from 2023 to 2030, and whether it can achieve the government’s target of 150,000 metric tonnes.

Financing remains a serious concern for firms and is considered the main hurdle in the growth and development of small and medium enterprises (SMEs). Recently, a new stream of financing (SCF; supply chain finance) has emerged to meet the financing issues of SMEs. Therefore, measuring SCF is essential to support SMEs’ operations. This study aims to develop and validate the SCF scale based on extant literature.

Using a mixed-method approach, this study recruited different samples of SME entrepreneurs to confirm the internal consistency, assess construct validity and check the item structure of the SCF scale in AMOS.

The outcomes of confirmatory factor analysis demonstrated the six factors of SCF (inventory financing, working capital optimization, reverse financing, fixed assets financing, logistics financing and order cycle financing) spread over 21 items. An interitem solid structure of the SCF scale offers invaluable contributions to the supply chain management literature.

This research supports SME entrepreneurs to obtain secure financing at the best cost, mitigating the risk of default, supporting the buyers’ payment terms, providing early payment to suppliers and strengthening the firm’s value chains. SMEs can obtain financing per their requirements to support their operational business processes. Moreover, SMEs can plan, manage and control finance-related transactional activities by correctly identifying financing solutions.

The present study contributes to SCM literature by developing and validating the SCF scale. To the best of the author’s knowledge, this is the first study that redefined SCF and identified its six dimensions.

For policymakers and participants of financial markets, predictions of trading volumes of financial indices are important issues. This study aims to address such a prediction problem based on the CSI300 nearby futures by using high-frequency data recorded each minute from the launch date of the futures to roughly two years after constituent stocks of the futures all becoming shortable, a time period witnessing significantly increased trading activities.

In order to answer questions as follows, this study adopts the neural network for modeling the irregular trading volume series of the CSI300 nearby futures: are the research able to utilize the lags of the trading volume series to make predictions; if this is the case, how far can the predictions go and how accurate can the predictions be; can this research use predictive information from trading volumes of the CSI300 spot and first distant futures for improving prediction accuracy and what is the corresponding magnitude; how sophisticated is the model; and how robust are its predictions?

The results of this study show that a simple neural network model could be constructed with 10 hidden neurons to robustly predict the trading volume of the CSI300 nearby futures using 1–20 min ahead trading volume data. The model leads to the root mean square error of about 955 contracts. Utilizing additional predictive information from trading volumes of the CSI300 spot and first distant futures could further benefit prediction accuracy and the magnitude of improvements is about 1–2%. This benefit is particularly significant when the trading volume of the CSI300 nearby futures is close to be zero. Another benefit, at the cost of the model becoming slightly more sophisticated with more hidden neurons, is that predictions could be generated through 1–30 min ahead trading volume data.

The results of this study could be used for multiple purposes, including designing financial index trading systems and platforms, monitoring systematic financial risks and building financial index price forecasting.

The study aims to investigate how enterprise information systems strategies-enabled strategy-making (ISS-SM) influences organizational agility (OA) via the mediated role of IT-enabled dynamic capabilities (ITDC) under environmental dynamism (ED). The study also investigates natural country moderation associated with the business context of the countries where the respondents are located might influence these relationships.

The study aims to investigate how enterprise ISS-SM influences OA via the mediated role of ITDC under ED. The study also investigates natural country moderation associated with the business context of the countries where the respondents are located that might influence these relationships.

The results demonstrate that ISS-SM influences ITDC to gain OA independent of the ED level. Indian and Brazilian firms show no different effects in the relationship of the research model. However, post hoc analysis revealed that strong ISS-SM on OA is fully mediated by ITDC under higher ED with a substantial coefficient of determination, more prominent for Indian firms characterized by young-age and middle-size firms, agribusiness and government sectors.

The fundamental to enabling practice and praxis of the strategy-as-practice approach to OA gains mediated through ITDC in different business context conditions.

The research contributes to extending the literature on the enterprise information systems strategy and information technologies capabilities.

The research aims to identify the impacts of strategic knowledge (SK) and information technology capabilities (ITC) on innovation ambidexterity (IAM) through business process performance (BPP).

The research framework is developed based on the theoretical grounding of resource orchestration (RO) (SK and ITC) impacts on IAM. The structural equation modeling (SEM) technique was used to test the research framework on a sample of 441 responses from Brazilian firms.

The results suggest that SK and ITC facilitate BPP, resulting in IAM. The findings also suggested differences in path coefficients in the SK and ITC of the business value generation process framework under environmental turbulence (ET). Finally, a strong SK of ITC is especially important in enabling BPP and IAM in large firms. Another case of most manufacturing and service firms demonstrated that both SK and ITC are essential to impacting IAM through BPP mediation.

The findings provide insight into how professionals can think and plan carefully to align SK and ITC for achieving balanced innovation and improving BPP in the dynamic business environment.

The study establishes a relationship between SK, ITC, BPP and IAM. The study developed novel constructs of SK and ITC and tested them, which gives new insight and links among the constructs.

This study responded to the call to empirically reconcile conflicting findings in unethical pro-organizational behavior (UPB) literature. It did so by examining the influence of organizational culture on the relationship between affective organizational commitment and UPB.

Using a sample of 710 U.S. service sector employees based on a three-wave data collection design, structural invariance assessment was utilized to evaluate the relationship between affective organizational commitment and UPB across organizational cultures with opposing effectiveness criteria (i.e. focused on stability vs flexibility).

The result indicated a statistically significant positive direct effect between affective organizational commitment and UPB for the stability-focused cultures, while finding a statistically insignificant effect for the flexibility-focused cultures. These results support organizational culture research, which shows that organizational cultures with opposing effectiveness criteria (i.e. stability vs flexibility) can either encourage or discourage ethical behavior.

While leaders and managers encourage employee commitment to the organization, it is important to understand that increased organizational commitment is not limited to positive outcomes. Cultivating elements of flexibility-oriented cultures, like promoting teamwork (as in clan cultures) or fostering innovation and adaptability (as in adhocracy cultures), can be a strategic approach to minimize the chances of UPB among committed employees.

By integrating insights from social exchange theory, Trevino’s interactionist model and the competing values framework, we have contributed to a nuanced understanding of how different organizational cultures can suppress or stimulate UPB.

The purpose of this study is to reveal the dynamics of house prices and sales in spatial and temporal dimensions across British regions.

This paper incorporates two empirical approaches to describe the behaviour of property prices across British regions. The models are applied to two different data sets. The first empirical approach is to apply the price diffusion model proposed by Holly et al. (2011) to the UK house price index data set. The second empirical approach is to apply a bivariate global vector autoregression model without a time trend to house prices and transaction volumes retrieved from the nationwide building society.

Identifying shocks to London house prices in the GVAR model, based on the generalized impulse response functions framework, I find some heterogeneity in responses to house price changes; for example, South East England responds stronger than the remaining provincial regions. The main pattern detected in responses and characteristic for each region is the fairly rapid fading of the shock. The spatial-temporal diffusion model demonstrates the presence of a ripple effect: a shock emanating from London is dispersed contemporaneously and spatially to other regions, affecting prices in nondominant regions with a delay.

The main contribution of this work is the betterment in understanding how house price changes move across regions and time within a UK context.