Search results

Organizations deal with digital technologies to achieve their strategic goals. The shift toward digitization is a major challenge because it requires companies to create a digital outlook that influences organizational design. As a result, investigation of institutional theory and entrepreneurial orientation theory in the European food industry has become the focus of research in recent times.

To this end, data were collected from 83 companies related to the food industry in the European context. By applying a hybrid phase of the partial least squares structural equation modeling (PLS-SEM) and artificial neural network (ANN) methods, this study captured the causal–non-linear relationships among the study constructs.

The findings revealed that the variables of institutional theory and entrepreneurial orientation theory affect the adoption of the digital strategy. There is also a dual interaction role for e-business capabilities and digital transformation. The results of non-linear relationships confirmed that digital strategy adoption is highly influenced by digital transformation, followed by risk-taking, digital leadership, e-business capabilities, organizational agility, proactiveness and innovativeness.

The authors provided significant implications for practitioners and academics about the most influential determinants of digital strategy – businesses must move swiftly toward digitization across its various units to achieve their objectives. An organization’s leadership must realize that equipping the employees with necessary skills is the first step toward digitalization.

The current study underscores the digital strategy, which is usually an overlooked area of investigation, in the food industry. The study identifies some important predictors of digital strategy adoption with the interaction’s role of digital transformation and e-business capabilities. Such relationships have been rarely discussed. In addition, the adoption of a hybrid SEM-AAN approach makes the study an original one.

There is an ongoing challenge to map the efficacy of e-retailing strategies in building both value co-creation opportunities for online customers and customer value for companies. Based on the service-dominant (S-D) logic, an integrative model is provided that connects the impact of convenience and personalisation strategies (CPSs) on an e-retailer's performance – by offering co-creation opportunities and customer engagement.

The survey instrument is validated and the model is tested with data from active online customers using a novel methodology that blends artificial neural network (ANN) analysis with partial least squares (PLS) in both the measurement model and the path analysis.

The findings robustly support the model and yield evidence of the contribution of CPSs in effective value propositions, the interface between the S-D logic and customer engagement, and the direct effect of customer engagement on tangible forms of value for companies.

This study is the first scholarly effort to provide a comprehensive understanding of how and why CPSs can maximise customer value for the e-retailer, while simultaneously testing the customer value/engagement interface with a new blended ANN-PLS method.

Brief introduction on the importance and the need for plastic analysis methods were presented in the beginning section of this review. The plastic method for analysis was considered to be the more advanced method of analysis because of its ability to represent the true behaviour of the steel structures. Then in the following section, a literature analysis has been carried out on the previous investigations done on steel plates, steel beams and steel frames by other authors. The behaviour of them under different types of loading were presented and are under the investigation of innovative new analysis methods.

Structure member connections also have the potential for plastic failure. In this study, the authors have highlighted a few topics to be discussed. The three topics in this study are T-end plate connections to a square hollow section, semi-rigid connections and cold-formed steel storage racks with spine bracings using speed-lock connections. Connection is one of the important parts of a structure that ensures the integrity of the structure. Finally, in this technical paper, the authors introduce some topics related to seismic action. Application of the Theory of Plastic Mechanism Control in seismic design is studied in the beginning. At the end, its in-depth application for moment resisting frames-eccentrically braced frames dual systems is investigated.

When this study involves the design of a plastic structure, the design criteria must involve the ultimate load rather than the yield stress. As the steel behaves in the plastic range, it means the capacity of the steel has reached the ultimate load. Ultimate load design and load factor design are the methods in the range of plastic analysis. After the steel capacity has reached beyond the yield stress, it fulfills the requirement in this method. The plastic analysis method offers a consistent and logical approach to structural analysis. It provides an economical solution in terms of steel weight, as the sections designed using this method are smaller compared with elastic design methods.

The plastic method is the primary approach used in the analysis and design of statically indeterminate frame structures.

This research aims to explore the factors influencing the adoption intention of eco-friendly smart home appliances among residents in densely populated urban areas of a developing country.

A quantitative research approach was employed to gather data from 348 respondents through purposive sampling. A comparative analysis strategy was then utilized to investigate the adoption of eco-friendly smart home appliances, combining both linear (PLS-SEM) and non-linear (fsQCA) approaches.

The results obtained from PLS-SEM highlight that performance expectancy, facilitating conditions, hedonic motivation, price value, and environmental knowledge significantly influence the adoption intention of eco-friendly smart home appliances. However, the findings suggest that effort expectancy, social influence, and habit are not significantly associated with customers' intention to adopt eco-friendly smart home appliances. On the other hand, the fsQCA results identified eight configurations of antecedents, offering valuable insights into interpreting the complex combined causal relationships among these factors that can generate (each combination) the adoption intention of eco-friendly smart home appliances among densely populated city dwellers.

This study offers crucial marketing insights for various stakeholders, including homeowners, technology developers and manufacturers, smart home service providers, real estate developers, and government entities. The findings provide guidance on how these stakeholders can effectively encourage customers to adopt eco-friendly smart home appliances, aligning with future environmental sustainability demands. The research implications underscore the significance of exploring the antecedents that influence customers' adoption intention of eco-friendly technologies, contributing to the attainment of future sustainability goals.

The environmental sustainability of smart homes, particularly in densely populated city settings in developing countries, has received limited attention in previous studies. Therefore, this study aims to address the pressing issue of global warming and make a meaningful contribution to future sustainability goals related to smart housing technologies. Therefore, this study employs a comprehensive approach, combining both PLS-SEM (linear) and fsQCA (non-linear) techniques to provide a more thorough examination of the factors influencing the adoption of environmentally sustainable smart home appliances.

India’s historical fiscal performance has featured elevated deficit levels. Driven by the imperative need for fiscal stimulus measures in response to the crisis, efforts toward fiscal consolidation from 2003 to 2008 were reversed in 2008–2009 due to the financial crisis. These stimulus actions are believed to have wielded a notable influence on inflation dynamics. Presumably, a high inflation rate hinders growth and inflicts severe welfare costs. Accordingly, the principal objective of this paper is to scrutinise the threshold effects of fiscal deficit on inflation within the context of the Indian economy.

We employed the Smooth Transition Autoregressive (STAR) Model, a robust tool for capturing non-linear relationships, to discern the specific threshold level of fiscal deficit. Our analysis encompasses annual data spanning from 1971 to 2020. Additionally, we have leveraged the Toda-Yamamoto causality test to establish the existence and direction of a causal connection between fiscal deficit and inflation in the Indian economy.

Our analysis pinpointed a critical threshold level of 3.40% for fiscal deficit, a value beyond which inflation dynamics in India undergo a marked transition, signifying the presence of significant non-linear effects. Moreover, the results derived from the Toda-Yamamoto causality test offer substantiating evidence of a causal relationship originating from the fiscal deficit and leading to inflation within the Indian economic framework.

The findings of our study carry significant implications, particularly for the formulation and execution of both fiscal and monetary policies. Understanding the threshold effects of fiscal deficit on inflation in India provides policymakers with valuable insights into achieving a harmonious balance between these two critical economic variables.

To the best of our knowledge, this study is the first of its kind to empirically investigate threshold effects of fiscal deficit on inflation in India from a non-linear perspective using the Smooth Transition Autoregression (STAR) model.

Standard economic theory predicts that any increase in public spending is accompanied by a rise in inflation in an economy. This paper presents empirical proof that prices do not always rise with an increase in public expenditure but only up to a certain threshold level. The primary aim of this paper is to unearth the government size-inflation nexus in India for the period from 1971 to 2019.

The logistic STAR (smooth transition autoregression) model is employed to unravel the government size-inflation nexus for the Indian economy from a non-linear perspective.

The finding of our study confirm the non-linear relationship between the size of the government and inflation in India. The estimated threshold level for government size is precisely found to be 9.27%. The size of the government exerts a negative influence on inflation until it reaches the optimal or threshold level. Any further increase in the size of government beyond this threshold level would result in a rise in inflation.

The findings have implications for the conduct of fiscal policy. Policymakers can increase government spending in a regime of small government size without having any inflationary impacts by generating revenues from taxes and other sources instead of relying much on the central bank. In the regime of a large-sized government, adhering strictly to the discipline in the conduct of fiscal and monetary policies would help curb inflation and enhance growth synchronously, hence alleviating any loss of welfare.

To the best of the authors’ knowledge, this study is an attempt to revisit the government size-inflation nexus in India from a non-linear perspective using the Smooth Transition Autoregression (STAR) model for the first time.

This study examines the linear and non-linear effects of enterprise risk management (ERM) and corporate governance (CG) on insurers’ risk-taking behaviour.

The study employed panel data of 63 insurers from South Africa over the period 2015 and 2019. The study used the generalised method of moments (GMM) to determine the direct relationship, while the dynamic panel threshold technique was utilised to discover whether there is non-linearity in the relationship and the threshold level at which ERM and CG stimulate insurance risk-taking.

The result from the GMM elicits a positive relationship between ERM and risk-taking, implying that insurers with a robust ERM system are more likely to pursue higher risks. The empirical evidence also suggests that board size and board independence improve insurers’ risk-taking. Contrarily, gender diversity shows an inverse relationship with risk-taking. The dynamic panel threshold regression confirms non-linearities between ERM, CG and risk-taking. The empirical evidence indicates a U-shaped relationship between ERM and risk-taking, implying that a robust ERM system increases insurers’ risk-taking and vice-versa. Further, board size and independence reveal an inverted U-shaped relationship, suggesting that larger boards and a higher proportion of independent directors exhibit lower risk-taking. However, gender diversity presents a negative relationship, demonstrating a strong impact at higher threshold levels. This tells that the presence of females on the board reduces insurers’ risk-taking preferences.

Due to the risk-bearing nature of the insurance business, it is required that they ensure a robust ERM system for prudent risk-taking decisions. This demands strict adherence to ERM principles and allocating sufficient resources for effective implementation. Also, there is a need for strong CG structures that pay more attention to diversity when selecting board members due to their influence in ensuring improved risk-taking choices.

This study contributes to the existing literature by providing insights into the under-researched role of ERM and CG in insurers’ risk-taking behaviour. The study further extends the literature by providing evidence on the non-linearity and threshold levels at which ERM and CG influence insurers’ risk-taking choices. The findings are unique and contribute to the growing body of literature documenting the need for strong ERM and CG systems in insurance companies.

As financial markets for environmentally friendly investment grow in both scope and size, analyzing the relationship between green financial markets and African stocks becomes an important issue. Therefore, this paper examines the role of infectious disease-based uncertainty on the dynamic spillovers between African stock markets and clean energy stocks.

The authors employ the dynamic spillover in time and frequency domains and the nonparametric causality-in-quantiles approach over the period of November 30, 2010, to August 18, 2021.

These findings are discernible in this study's analysis. First, the authors find evidence of strong connectedness between the African stock markets and the clean energy market, and long-lived but weak in the short and medium investment horizons. Second, the BDS test shows that nonlinearity is crucial when examining the role of infectious disease-based equity market volatility in affecting the interactions between clean energy stocks and African stock markets. Third, the causal analysis provides evidence in support of a nonlinear causal relationship between uncertainties due to infectious diseases and the connection between both markets, mostly at lower and median quantiles.

Considering the global and recent use of clean energy equities and the stock markets for hedging and speculative purposes, one may argue that rising uncertainties may significantly influence risk transmissions across these markets. This study, therefore, is the first to examine the role of pandemic uncertainty on the connection between clean stocks and the African stock markets.

The purpose of this paper is to study the shearing performance under bi-directional loading of an interior beam–column joint (BCJ) sub-assemblage using the finite element analysis (FEA) tool (midas fea), validated in this research.

The BCJ can be defined as an essential part of the column that transfers the forces at the ends of the members connected to it. The members of the rigid jointed plane frame resist external forces by developing twisting moment, bending moment, axial force and shear force in the frame members. On the type of joints, the response to the action of lateral loads depends on reinforced concrete (RC) framed structures. The joint is considered rigid if the angle between the members remains unchanged during the structural deformation. This work examined the shear deformation, load displacement and strength of a non-seismically detailed internal concentric RC joint using non-linear FEA. The bi-directional loading imposes the oblique compression zone on one joint corner. This joint core’s oblique compression strut mechanism differs significantly from that under unidirectional loading. The numerical results are compared with experimental results in this study, with the data published in the literature.

Numerical analysis results show that, in the comparative study of numerical and experimental values, the FEA tool predicts the behaviour of the RC BCJ well. The discrepancy between the experimental and numerical results amounts to 6 to 12% end displacement of the beam, 7% resultant joint shear force, 4.23% column bar strain and 0.70% hoop strain.

The current code of practice describes the joint sub-assemblage behaviour along the single axis individually. In the non-orthogonal system, the superposition of the two axes for joint space results in overlapping the stresses and, hence, the formation of the oblique strut. This may result in a reduction in the joint capacity under bi-directional loading. The behaviour must be explored in depth, and an attempt is made for further exploration.

There will probably be nine billion people on the earth by 2050, meaning food consumption will rise dramatically. Pakistan, the fifth most populous nation in the world, is rapidly expanding its population, making it difficult for the nation to sustain its food supply. Unfortunately, the country's focus on ensuring food security has not kept up with the demographic shifts in its population. However, innovative solutions are sorely needed in the face of several worldwide problems, especially in the crucial agriculture sector. This underscores the need to integrate sustainable financial practices. Considering these circumstances, this research thoroughly examines the intricate relationship inside Pakistan between financial stability (FS), agricultural subsidies, and productivity. Acknowledging the underlying intricacies and asymmetries at work, this study aims to analyze the complex relationships influencing the nation's agricultural production.

The research tries to shed light on the subtle processes at the intersection of financial stability, agricultural subsidies, and agricultural productivity through a comprehensive investigation of these multiple challenges. A non-linear autoregressive distributive lag (NARDL) technique is used, using a dataset from 1980 to 2022.

The results show that FS has a mixed impact on agricultural productivity, both positive and negative. Increasing FS_POS has a beneficial influence on agricultural output, linked to a notable 1.404% increase in output. On the other hand, increasing FS_NEG causes a significant 11.441% decrease in agricultural output, demonstrating its negative impact on output. Subsidies for agriculture also have asymmetric impacts; SUB_POS and SUB_NEG influence variations in agricultural productivity. A substantial 2.414% rise in agricultural output is shown by SUB_POS, demonstrating its noteworthy beneficial influence. Conversely, SUB_NEG adds a relatively small increase of 1.659% in agricultural output. However, the different amounts of each person's contribution show how subtle their effects are.

The current study is limited to the relationship between financial stability, agricultural subsidies, and agricultural productivity, considering the inherent complexity and asymmetries at work in Pakistan only. Further studies are required in Asian markets to have a bigger picture of the agricultural sector.

Considering these critical empirical findings, the report recommends strategic strategies to promote long-term agricultural growth in Pakistan. These include providing integrated financial services customized to farmers' needs, such as credit, insurance, and savings alternatives. Transparency and efficiency in procedural frameworks and the formation of efficient public-private partnerships should be prioritized. Furthermore, improving agricultural subsidy schemes emerges as a crucial priority. Targeting marginalized farmers more effectively and optimizing distribution through transparent, digitally driven systems can significantly improve program performance.