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The paper aims to propose an innovative two-stage decision model to address the sustainable-resilient supplier selection and order allocation (SSOA) problem in the single-valued neutrosophic (SVN) environment.

First, the sustainable and resilient performances of suppliers are evaluated by the proposed integrated SVN-base-criterion method (BCM)-an acronym in Portuguese of interactive and multi-criteria decision-making (TODIM) method, with consideration of the uncertainty in the decision-making process. Then, a novel multi-objective optimization model is formulated, and the best sustainable-resilient order allocation solution is found using the U-NSGA-III algorithm and TOPSIS method. Finally, based on a real-life case in the automotive manufacturing industry, experiments are conducted to demonstrate the application of the proposed two-stage decision model.

The paper provides an effective decision tool for the SSOA process in an uncertain environment. The proposed SVN-BCM-TODIM approach can effectively handle the uncertainties from the decision-maker’s confidence degree and incomplete decision information and evaluate suppliers’ performance in different dimensions while avoiding the compensatory effect between criteria. Moreover, the proposed order allocation model proposes an original way to improve sustainable-resilient procurement values.

The paper provides a supplier selection process that can effectively integrate sustainability and resilience evaluation in an uncertain environment and develops a sustainable-resilient procurement optimization model.

Existing classifications of inflationary regimes often rely on subjective judgments, hindering objectivity and accuracy. This study proposes a novel, data-driven approach to address this limitation.

We combine unsupervised clustering and classification tree methods to analyze Argentine inflation data from 1943 to 2022. Two smoothing techniques are introduced: a measure of temporal contiguity and a rolling majority rule method. The resulting regimes are compared to existing classifications based on their explanatory power for inflation-relative price variability.

Our method identifies distinct inflationary regimes, demonstrating significant improvement in objectivity and accuracy compared to existing literature. The regimes capture key historical periods and exhibit a strong association with inflation-relative price variability, providing valuable insights into Argentine inflation dynamics.

This study offers a novel methodological framework for constructing objective and accurate inflationary regimes, free from subjective biases. This approach holds potential for application to other contexts and contributes to a more nuanced understanding of inflation dynamics.

Whereas research on brand value co-creation is being conducted from a number of different perspectives, the psycho-social mechanisms that motivate consumers towards brand value co-creation have room for theory development. The purpose of this paper is to contribute to the literature on brand value co-creation in luxury consumption by analysing the role of a number of psychological constructs that impact consumers’ proclivity towards brand value co-creation.

The data for this study were collected through a large-scale questionnaire-based design and were evaluated using a multivariate statistical analysis technique.

The results show that the need for autonomy, the need for belonging and the need for uniqueness mediate the relation between consumers’ self-concept and proclivity towards brand value co-creation. The findings indicate that luxury brand managers need to develop a critical mix of co-creational strategies in a way in which the brand harmoniously satisfies a need for relational identity co-creation while also providing varied heterogenous interactions.

This research has been conducted in an emerging market of Asia, thus providing insights into what motivates co-creation in an under-researched but lucrative market segment. The Socio-Economic Class A of emergent countries has an inelastic purchasing power and disposable income to consume luxury brands. Only by understanding the underlying purchase motivations of these consumers can brand managers effectively benefit from their co-creation endeavours.

This paper aims to explore the integration of technology platforms of Fintech service providers in Bangladesh and its outcome on the users’ acceptance and intention to use it. It has considered Bangla QR, a Bangladesh-specific unified payment platform, for the said purpose.

The study investigates the usage and acceptance of Bangla QR’s financial service application by collecting data on user demographics and usage patterns. The data have been collected from Bangla QR users who have firsthand experience using a unified payment system in Bangladesh. Structural equation modeling (SEM) has been used to analyze the data and investigate the relationships between latent constructs, such as Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), Facilitating Conditions (FC), Hedonic Motivation (HM) and Behavioral Intention (BI).

Results reveal that the experience of users from earlier platforms and their expectation of improved technologies have a positive and significant relationship with the behavior intention of users regarding the acceptance of a fresh fintech platform. Specifically, PE, EE and FC have a significant effect on HM. Moreover, HM mediates the relationship between PE and BI, EE and BI and FC and BI. Startlingly, SI does not affect HM and BI.

The study posits that users of Bangla QR are embracing the unified payment scheme willingly. Experience with new technology in the financial aspects has given them the confidence to continue this in the future. However, social awareness about the intrinsic worth of unified payment should be raised by financial institutions and regulators in Bangladesh. The lack of Bangla QR’s accessibility limits the number of respondents, and its newness hinders the researchers from focusing on price value and habit constituting BI.

First, this is the first paper to explore the fintech users’ intention to continue a unified payment platform in Bangladesh. Second, it uses the UTAUT2 model with modifications as per the requirement of the study. Finally, this study theorizes specific reasons to develop the overall scenario of a unified payment system in the future in Bangladesh.

There are two main objectives in this study. First, we aim to develop a set of constructs for health task management support (HTMS) features to evaluate which health-related tasks are supported by mobile health application (mHealth app) functions. Second, drawing on innovation resistance theory (IRT), we examine the impacts of the newly developed HTMS dimensions on perceived usefulness, alongside other barrier factors contributing to technology anxiety.

Using a mixed-method research design, this research seeks to develop new measurement scales that reflect how mHealth apps support older adults’ health-related needs based on interviews. Subsequently, data were collected from older adults and exploratory factor analysis was used to confirm the validity of the new scales. Partial least squares structural equation modeling (PLS-SEM) was used to analyze survey data from 602 older adults.

The PLS-SEM results indicated that medical management task support, dietary task support, and exercise task support were positively associated with perceived usefulness, while perceived complexity and dispositional resistance to change were identified as antecedents of technology anxiety. Perceived usefulness and technology anxiety were found to positively and negatively influence adoption intention, respectively.

This study enriches the information systems literature by developing a multidimensional construct that delineates how older adults’ health-related needs can be supported by features of mHealth apps. Drawing on IRT, we complement the existing literature on resistance to innovation by systematically examining the impact of five types of barriers on technology anxiety.

Despite being a fundamental concept, the field of supply chain management (SCM) exhibits a significant lack of consensus regarding the definition of supply chain flows (SCFLOWS). Additionally, there has been an over-reliance on three flows – material, information and finance – while various other flows crucial to SCM performance have been overlooked. Hence, the purpose of this study is twofold: (1) to explore the multi-dimensional nature of SCFLOWS and (2) to identify additional flows beyond the commonly acknowledged ones that are vital for SCM performance.

This study employs various qualitative methods as part of the abduction process. The methods include in-depth interviews with logistics professionals, a Delphi study involving SCM scholars and a focus group comprising airline industry practitioners.

Seven SCFLOWS dimensions are identified and presented as SCFLOWS framework. Also, two additional flows, i.e. human and capital equipment, are proposed as vital to SCM performance.

This is the first study to introduce SCFLOWS framework to achieve consensus in the field. By introducing two additional flows, it proposes extending the SCFLOWS boundary to include various flows overlooked previously but pertinent to SCM performance. The SCFLOWS framework serves as a systematic guide to validate additional flows and represents an important step towards building SCM theory.

This work aims to develop a multi-step approach for the unified integration of 3D construction printing (3DCP) and building information modeling (BIM), allowing users to easily and automatically outline the instructions for 3D printing of buildings starting from BIM model and ensuring the wide spread of this new technology in Civil Engineering sector.

The proposed methodology exploits Revit for 3D modeling and BIM, using Dynamo as a programming interface for generating G-code.

The paper demonstrates how the proposed methodology can extract information from a BIM model to support building construction using digital fabrication techniques. This code guides the printer’s movements and operations, specifying the path, speed, layers and essential parameters to construct concrete structures layer by layer. It transforms digital designs into precise and efficient physical structures.

This work allows overcoming some of the current limitations associated with bridging BIM models to 3D construction printing. The proposed approach integrates BIM and 3DCP. If the model undergoes changes in the BIM model, the proposed system allows for automatic updates in the 3D printing files. Furthermore, the possibility offered by the proposed methodology to test the G-code on a scaled model allows for the correction of any errors before printing on a large-scale machine.

The novelty of the proposed approach is threefold: i) A new unified integration methodology for BIM and 3D construction printing is defined; ii) An example of a 3D printed building unit is modeled with BIM, incorporating various discipline models such as Architecture, Structure, and Mechanical, Electrical, Plumbing (MEP) systems; iii) The proposed approach allows for testing the G-code at scale before printing with a full-scale machine.

This paper assesses the environment of legitimacy by determining the role of institutional quality and policy uncertainty on the performance of five major South Asian stock markets (India, Pakistan, Bangladesh, Sri Lanka, and Nepal) using 21 years data from 2000 to 2020. The focus of this study is to approach the issue of the environment of legitimacy that leads to sustained market returns.

Panel cointegration tests of Kao and Pedroni are applied, and the Dynamic Panel Vector Autoregressive (PVAR) model is used to determine the estimates.

ADF P-Values of both Kao and Pedroni tests show that the panels are cointegrated; the statistical significance of the results of the Kao and Pedroni panel cointegration test confirms cointegration among the variables. After determining the most appropriate lag, the analysis is done using PVAR. The results indicate that institutional quality, policy uncertainty, and GDP positively affect stock market return. Meanwhile, government actions and inflation negatively affect stock market returns. On the other hand, stock market return positively affects institutional quality, government action, policy uncertainty, and GDP. While stock market return negatively affects inflation.

The sample is taken only from a limited number of South Asian countries, and the period is also limited to 21 years.

Based on our research findings, we have identified several policy implications recommended to enhance and sustain the performance of stock markets.

This paper uses a unique analytical tool, which gives a better insight into the problem. The value of this work lies in its findings, which also have practical implications and theoretical significance.

To the best of the authors’ knowledge, this study is the first to investigate the intricate relationship between crude oil spot and futures prices, focusing on both cointegration and market efficiency during the COVID-19 pandemic, and the beginning of the Russia–Ukraine conflict. Using daily West Texas Intermediate data from January 2020 to March 2024, like Cunado and Pérez de Gracia (2003), the authors use advanced statistical methods to identify structural breaks and assess cointegration levels. Linear and nonlinear Granger causality tests are used to reveal underlying dynamics.

This paper uses the Lagrange Multiplier test by Arai and Kurozumi (2007) to check for cointegration with various shifts in crude oil spot and futures markets. The two-step procedure by Kejriwal and Perron (2010) and Kejriwal et al. (2022) is then applied to assess partial parameter stability in cointegration models. Efficiency is examined using both bivariate and trivariate models based on non-arbitrage and expectations hypotheses. Finally, causality is analyzed with the vector error correction model for linear Granger causality, and the tests by Bai et al. (2018) and Diks and Panchenko (2006) for nonlinear causality.

The analysis reveals that futures prices generally lead spot prices through both linear and nonlinear causality during certain periods, while only linear causality is present in others. This inconsistency suggests fluctuating market efficiency and potential arbitrage opportunities. Structural breaks indicate that the equilibrium between spot and futures prices adjusts in response to significant events like the COVID-19 pandemic and the Russia–Ukraine war. The study identifies specific periods, particularly between January 2020 and March 2024, where both linear and nonlinear forecasting between futures and spot oil prices are effective, highlighting the dynamic nature of their relationship.

Despite extensive efforts, pinpointing the exact break date for COVID-19 remains challenging due to limitations in the data set and methodology. Additionally, the analysis of the Russia–Ukraine conflict is still ongoing. These challenges highlight the complexity of addressing structural breaks linked to unprecedented events.

The findings offer valuable insights for both academia and industry practitioners. The study reveals potential arbitrage opportunities stemming from inconsistent market efficiency and fluctuating causality between futures and spot prices, allowing traders to optimize their trades and timing. It also enhances risk management by identifying when linear and nonlinear causality is most effective. Policymakers can use these insights to evaluate market stability, especially during major disruptions such as the COVID-19 pandemic and geopolitical conflicts, guiding regulatory decisions. Furthermore, the study highlights the importance for investors to adjust their strategies in response to structural breaks and evolving market conditions.

This study’s social implications are diverse, extending beyond finance and academia. It influences economic stability by revealing inefficiencies and arbitrage opportunities in crude oil markets, aiding better resource allocation. Enhanced transparency benefits stakeholders, promoting fair market practices and consumer protection. Policymakers can refine regulations based on identified structural breaks, ensuring market stability. The study indirectly impacts environmental discussions by examining crude oil’s link to global energy consumption. Financially, it guides investment strategies, influencing resource distribution and the broader economy. Additionally, its educational contribution stimulates academic discourse, fostering growth in energy economics and financial market knowledge, shaping future research.

The originality and value of this paper lie in its comprehensive examination of the dynamic relationship between futures and spot oil prices, particularly through both linear and nonlinear causality across different periods. By identifying and analyzing periods of both linear and nonlinear causality, the study uncovers fluctuating market efficiency and potential arbitrage opportunities that are not typically addressed in conventional analyses. Additionally, the paper’s focus on the impact of significant global events, such as the COVID-19 pandemic and the Russia–Ukraine war, on the equilibrium between spot and futures prices offers a novel perspective on how structural breaks influence market dynamics. This nuanced understanding enhances both theoretical and practical knowledge, offering valuable insights for traders, investors and policymakers to navigate and respond to evolving market conditions.

This study aims to explore the role of international ambidexterity (IA) in improving the innovation capability of emerging market multinationals. In particular, the main purpose of this research is to study the relationship amongst digitalisation, IA and innovation performance (IP) amongst multinational enterprises in China’s healthcare industry.

The data for this investigation were collected from 134 listed companies in China’s healthcare industry during the study period. This study tested the hypotheses by constructing a two-way fixed-effects model.

The results show that both the balance dimension and the combined dimension of IA have significant positive effects on IP. Digitalisation not only has a direct positive effect on IP but also positively moderates the positive correlation between IA and IP.

Previous studies have not captured the relationship between ambidexterity, digitalisation and IP, and this study helps to fill in the gap and examine these associations in China’s healthcare industry. The results of this study provide valuable insights for healthcare industry managers to understand the role of ambidexterity and digitalisation in innovation in the context of internationalisation.