Search results

Distributed photovoltaic (DPV) projects generally have output risks, and the production effort of the supplier is often private information, so the buyer needs to design the optimal procurement contract to maximise its procurement utility.

Based on the principal-agent theory, we design optimal procurement contracts for DPV projects with fixed payments and incentive factors under three situations, i.e. symmetry information, asymmetry information without monitoring and asymmetry information with monitoring. We obtain the optimal production effort and expected utility of the supplier, the expected output and expected utility of the buyer and analyse the value of the information and monitoring.

The results show that under asymmetric information without monitoring, risk-averse suppliers need to take some risk due to output risk, which reduces the optimal production effort of the supplier and the expected output and expected utility of the buyer. Therefore, when the monitoring cost is below a certain threshold value, the buyer can introduce a procurement contract with monitoring to address the asymmetry information. In addition, under asymmetric information without monitoring, the buyer should choose a supplier with a low-risk aversion.

Considering the output risk of DPV projects, we study the optimal procurement contract design for the buyer under asymmetric information. The results provide some theoretical basis and management insights for the buyer to design optimal procurement contracts in different situations.

Despite the government’s efforts to protect domestic industries from foreign competition through tariffs, the industrial sector’s contribution to GDP continued to decline in Nigeria. Based on the scenario, this study assessed the symmetric and asymmetric effects of tariffs on industrial performance in Nigeria for the period 1988–2021. Tariff was captured with a tariff rate applied to the weighted mean of all products, while industry value added as a percent of GDP was used as a proxy for industrial performance.

Linear and nonlinear ARDL techniques were used for the analysis.

The symmetric (linear ARDL) results revealed that tariffs have a significant positive effect on industrial performance in both the short and long term. The asymmetric (nonlinear ARDL) results showed that a long-term asymmetry exists between tariffs and industrial performance. It revealed positive effects on industrial performance for both positive and negative tariff changes, with the negative change having a greater impact.

Generally, the results showed that the use of tariffs to protect domestic industries in Nigeria promotes industrial performance. The implication is that the declining contribution of the industrial sector to GDP in Nigeria is not a result of the tariff policy. It shows that the government should look beyond tariff policy to enhance the industrial contribution to GDP.

Nigeria should exercise caution in using tariff policies to protect domestic industries to avoid retaliation from their trade partners that could reverse the positive impacts.

This pragmatic investigation examines the dynamic nexus between crude oil prices and food inflation from South and Southeast Asian perspectives.

This study investigates the asymmetric effects of global crude oil prices on food inflation using a nonlinear autoregressive distributed lag (ARDL) model with monthly data covering the period from May 2012 to April 2022.

The empirical evidence reveals that international crude oil has a substantial impact on food prices in the majority of countries. Additionally, the relevant outcome documents that the asymmetric effect of global crude oil on food inflation applies to Sri Lanka and Vietnam, while in the other countries, it is symmetric.

Considering the optimistic outcomes, this empirical investigation is certain to have important shortcomings. Initially, the conclusions drawn from the above findings were based only on detailed assessments of the aforementioned variables' data over a 10-year period. The current scholarly analysis investigates the existence of an asymmetric impact of crude oil on food inflation, limited to six Asian countries. On the other hand, considering a greater number of Asian economies could enhance the analysis’s robustness and precision.

The current research aims to contribute to the existing literature on food inflation and global oil prices in the following ways: First, this study investigates the nexus between global crude oil and food inflation in a novel way, considering the nonlinear relationship between the variables. To figure out the nonlinear relationship or uneven effect of the global oil shock on food prices, we use the nonlinear ARDL model. Secondly, as food inflation is one of the major issues for the South and Southeast Asian economies, this empirical investigation broadens the analysis by incorporating a perspective from South and Southeast Asia, an area largely overlooked by previous researchers. Finally, we are very optimistic about the phenomenal contribution of current analysis to comprehending the conception of oil and food price dynamics from a broader perspective to achieve the Sustainable Development Goal (SDG), which aims for a sustainable resolution to end hunger in all its forms by 2030 and to accomplish food security, especially in emerging economies.

The current study aims to explore the determinants of user intentions towards fitness YouTube channels, employing the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) and Uses and Gratifications Theory (UGT) as theoretical frameworks.

Symmetric and asymmetric analyses were employed for data analysis, utilizing partial least squares-structural equation modeling (PLS-SEM) for symmetric analysis and fuzzy-set qualitative comparative analysis (fsQCA) for asymmetric analysis.

The study revealed significant impacts of most UTAUT2 determinants and all UGT determinants on user intentions. Additionally, the fsQCA results supported the concept of equifinality, indicating that various configurations of causal combinations can predict a high level of behavioral intention. These findings underscore the significance of comprehending user motivations and factors related to technology and social media in the context of maintaining or increasing followership and viewership for fitness content providers.

The findings suggest that individuals with high expectations and facilitating conditions, as per UTAUT, and heightened hedonic and socializing motivations, in line with UGT, are more inclined to follow fitness YouTube channels. This study offers valuable insights for fitness content creators and marketers navigating the complexities of the digital age.

This paper aims to investigate whether negative and positive monetary policy (MP) shocks have asymmetric impacts on corporate firms’ investment decisions in Pakistan using firm-level panel data set. Moreover, the authors emphasized on symmetric effects of MP; the authors examine whether high-leverage and low-leverage firms respond differently to negative and positive unanticipated shocks in MP instruments.

In contrast to the conventional framework of VAR, it uses an alternative methodology of Taylor rule to estimate unanticipated MP shocks. The two-step system-generalized method of movement (GMM) estimation method is applied to examine the effect of MP shocks on firm investment through leverage-based asymmetry.

The two-step system-GMM estimation results indicate that unanticipated negative changes (unfavorable shocks) in MP instruments have negative, significant effects on investment. In contrast, unanticipated positive changes (favorable shocks) have statistically insignificant impacts on firm investment. The results also reveal that firm leverage has a significant role in establishing the effect of unanticipated negative changes in MP instruments on investments. Finally, the results indicate that high-leverage firms respond more to negative changes than low-leverage firms. Yet, the results show that only low-leverage firms positively respond to unanticipated positive shocks in MP.

The findings of the paper suggest that MP authorities should pay due attention to the asymmetric effects of MP shocks on firm investment while designing MP. Because firm leverage has a significant influence on the effects of MP shocks, firm managers should take into account such role of leverage while deciding capital structure of their firms.

First, unlike “Keynesian asymmetry” and most of published empirical research work, the authors use both unanticipated negative and positive MP shocks simultaneously. Departing from the conventional empirical literature, the authors differentiate between unanticipated positive and negative shocks in MP using the backward-looking Taylor rule. Second, the authors contribute to the existing literature by investigating the differential effects of positive and negative unanticipated MP shocks on firms’ investment decisions. Unlike the published studies that have emphasized on the symmetric effects of MP, the authors examine whether high-leverage and low-leverage firms respond differently to negative and positive unanticipated shocks in MP instruments.

Drawing upon the natural resource-based view (NRBV), the present study explores the role of green dynamic capability (GDC) as a mediating variable in the relationship between green transformational leadership (GTL) and green innovation (GI) in the hotel industry. The research further assesses green performance (GP) as a resultant factor of GI.

The research was conducted in Italian luxury hotels to assess the efficacy of our conceptual framework among workers in the hospitality industry. The study utilized a three-wave, two-week time-lagged design (N = 303). In addition, the study also intends to apply partial least squares structural equation modeling (PLS-SEM) and fuzzy qualitative comparative analysis (fsQCA) to have distinctive discernment into model rapport.

The results of the study indicate the linkage between GTL and GI. Furthermore, the study also found the partial mediation of GDC. The results show numerous combinations using fsQCA that can be utilized to increase GP.

There is little empirical evidence to study GTL and GI in hospitality studies. This work empirically investigates GTL, GDC and GI relationships to fill a knowledge gap. It also explains undiscovered factors and provides causal recipes to improve GP using fsQCA.

This paper aims to apply several data mining techniques for predicting the daily precision improvement of Jakarta Islamic Index (JKII) prices based on big data of symmetric volatility in Indonesia’s Islamic stock market.

This research uses big data mining techniques to predict daily precision improvement of JKII prices by applying the AdaBoost, K-nearest neighbor, random forest and artificial neural networks. This research uses big data with symmetric volatility as inputs in the predicting model, whereas the closing prices of JKII were used as the target outputs of daily precision improvement. For choosing the optimal prediction performance according to the criteria of the lowest prediction errors, this research uses four metrics of mean absolute error, mean squared error, root mean squared error and R-squared.

The experimental results determine that the optimal technique for predicting the daily precision improvement of the JKII prices in Indonesia’s Islamic stock market is the AdaBoost technique, which generates the optimal predicting performance with the lowest prediction errors, and provides the optimum knowledge from the big data of symmetric volatility in Indonesia’s Islamic stock market. In addition, the random forest technique is also considered another robust technique in predicting the daily precision improvement of the JKII prices as it delivers closer values to the optimal performance of the AdaBoost technique.

This research is filling the literature gap of the absence of using big data mining techniques in the prediction process of Islamic stock markets by delivering new operational techniques for predicting the daily stock precision improvement. Also, it helps investors to manage the optimal portfolios and to decrease the risk of trading in global Islamic stock markets based on using big data mining of symmetric volatility.

This research is a pioneer in using big data mining of symmetric volatility in the prediction of an Islamic stock market index.

The reported Kullback–Leibler (K–L) distance-based generalized grey target decision method (GGTDM) for mixed attributes is an asymmetric decision-making basis (DMB) that does not have the symmetric characteristic of distance in common sense, which may affect the decision-making result. To overcome the deficiency of the asymmetric K–L distance, the symmetric K–L distance is investigated to act as the DMB of GGTDM for mixed attributes.

The decision-making steps of the proposed approach are as follows: First, all mixed attribute values are transformed into binary connection numbers, and the target centre indices of all attributes are determined. Second, all the binary connection numbers (including the target centre indices) are divided into deterministic and uncertain terms and converted into two-tuple (determinacy and uncertainty) numbers. Third, the comprehensive weighted symmetric K–L distance can be computed, as can the alternative index of normalized two-tuple (deterministic degree and uncertainty degree) number and that of the target centre. Finally, the decision-making is made by the comprehensive weighted symmetric K–L distance according to the rule that the smaller the value, the better the alternative.

The case study verifies the proposed approach with its sufficient theoretical basis for decision-making and reflects the preferences of decision-makers to address the uncertainty of an uncertain number.

This work compares the single-direction-based K–L distance to the symmetric one and uses the symmetric K–L distance as the DMB of GGTDM. At the same time, different coefficients are assigned to an uncertain number’s deterministic term and uncertain term in the calculation process, as this reflects the preference of the decision-maker.

Is there any symmetric or asymmetric connection between mobile wallet service quality (MWSQ) dimensions and loyalty intention? Are there any factors that intervene in the relationship between MWSQ and loyalty intention? To answer these questions, the present study explored dimensions of MWSQ and proposed a novel framework to comprehend symmetric and asymmetric relationship between MWSQ dimensions and loyalty intention.

The study used data from 422 m-wallet users. Structural equation modeling (SEM) was used to investigate the impact of MWSQ dimensions (reliability, security, responsiveness, practicity and design) on loyalty intention. Furthermore, fuzzy sets qualitative comparative analysis (fsQCA) has also been applied to understand the complex, non-linear and synergistic effects of MWSQ dimensions on brand loyalty that SEM failed to reveal.

Using structural equation modeling (SEM) and fuzzy sets qualitative comparative analysis (fsQCA), current study revealed three major findings. First, except for practicity and design, results revealed a significant positive impact of MWSQ dimensions (reliability, security and responsiveness) on loyalty intention. Second, the study found that association between MWSQ dimensions (reliability, security and responsiveness) and loyalty intention was partially mediated by two parallel mediators namely brand image and brand satisfaction. Third, fsQCA uncovered asymmetric, synergistic and non-linear effects of MWSQ dimensions on loyalty intention that SEM failed to reveal. It revealed six sufficient conditions for determining low and high loyalty intention. Predictive validity has been also tested to determine accuracy of fsQCA results.

For practitioners, the proposed model is helpful as it will facilitate them in taking an edge over competitors by emphasising on key MWSQ dimensions. It will enable them to frame effective strategies for increasing market share and customer retention.

It is among the pioneer studies which explored the service quality dimensions of m-wallet, and used combination of both quantitative and qualitative techniques to propose an integrated framework for m-wallet service quality.

The purpose of this study is to identify the dynamic parameters of journal bearings in asymmetric rotor systems without additional test runs or excitations.

An asymmetric rotor-bearing test rig was set up for the identification experiment. Comparations were made between the measured response of the asymmetric rotor and the symmetric rotor. The mathematical model of the asymmetric rotor is established by the finite element method. The identification algorithm is based on the model of the rotor and the measured vibration response to identify bearing parameters. The influence of modeling error and measurement noise on the identification results are numerically analyzed. The dynamic parameters of the journal bearings under different rotational speeds are identified and compared with the theoretical values calculated by the perturbation method.

The experiment results show that the vibration characteristics of the asymmetric rotor and the symmetric rotor are different. The numerical evaluation of the identification algorithm shows that the algorithm is accurate and has good robustness to modeling error and measurement noise. The identified dynamic parameters agree reasonably well with the parameters derived from the theoretical bearing model.

The proposed identification method uses the unique vibration characteristics of asymmetric rotors to identify the bearing dynamic parameters. As the method does not require excitations or additional test runs, it is suitable for the field test.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-03-2024-0096/