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Purpose – The aim of this study is to analyze the effects of macro- and micro-economic variables on the ratio of troubled financing (Non-Performing Financing, NPF).

Design/Methodology/Approach – The method used in this research is the data panel fixed effect with 13 banks and 4 periods of data report (semi-annual report 2014–2015).

Findings – The regression result achieved that variable inflation significantly influences the ratio of NPF. Variable Gross Domestic Product and assets total significantly influence the ratio of NPF too. While the SBI sharia’s variable and Financing to Deposit Ratio did not significantly affect NPF in Syariah’s Unit of Aceh Bank Pembangunan Daerah (BPD) in Indonesia.

Research Limitations/Implications – This study uses panel data which are a combination of time series data and cross-section.

Practical Implications – The policymakers can design a macro-policy carefully and better fiscal policies.

Originality/Value – This research was conducted on the Syariah’s Unit of BPD in Indonesia during the period 2014–2015 and it has not been done before.

This study aims to examine the relationship between global renewable energy consumption and economic growth in Gulf Cooperation Council (GCC) countries from 2001 to 2019.

This paper used a panel regression model to study the six GCC countries over the period from 2001 to 2019.

As expected, the findings indicated a significant and negative relationship between global renewable energy consumption and GCC economic growth. Additionally, there was a positive and significant relationship between GCC economic growth and the control variables, specifically labor, capital, CO₂ emissions and non-renewable energy production.

The results are of great importance to policymakers in GCC oil-exporting countries, as expected growth in renewable energy consumption will lower their economic growth in the future. Hence, they should first diversify their economy and lower their dependence on oil. Second, these countries can invest in solar energy through international joint ventures, especially with North African countries in close proximity to Europe, to become leaders in solar energy production.

How global energy consumption is related to GCC countries’ economic growth remains unclear, not only in GCC countries but also in many oil-exporting countries around the world, so future studies are needed. Furthermore, GCC governments will be able to create appropriate policies for the green economy and achieve their objectives if they have a comprehensive understanding of how global growth in renewable energy demand affects GCC economies.

Purpose – The purpose of this research is to study the process conditions that give best yield and expected compositions of liquid smoke products that result during the pyrolisis process relying on predetermined variables.

Design/Methodology/Approach – Pyrolisis process running times are varied, that is, 0.5, 1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5, 5.5, and 6 hourly. Condensing temperature maintained remained 25–30 °C. Products identification was applied by using gas chromotography mass spectroscopy.

Findings – Based on the research output, it was concluded that process conditions which give maximum yield were achieved when using double unit condenser (DUC) and time optional four hours, and it provides maximum volume liquid smoke product, and compositions of pyrolisis products. The process also created seven components, namely nepthalene, propanoic acid, 3,7 nanodiena, 2 metilguaiakol, 2-metoksi 4-methyl phenol, 4 ethyl-2 metoksil phenol, oxybanzene. Applying DUC during condensation phase may increase condensing force thereafter obtaining resulted products between 200% and 300% rather than using single unit condenser (SUC).

Research Limitations/Implications – This research was conducted on a fixed batch reactor made of a metal plate with a thickness of 3.0 mm. It carries 200 kg in capacity. In this phase, the moisture of candlenut shells might be kept in 10–12.5% wt. Process temperature applied ranged within 350–500 °C.

Originality/Value – In addition the study increased the theorical of understanding about pyrolisis process and Improving the production of liquid smoke from candlenut shell by pyrolisis process using the method of vapor condensation (Double unit condensor).

The purpose of this paper is to provide insight into the students’ adoption rate of the online examinations at Universitas Terbuka (UT), which is a hotbed of technological innovation in higher education, by analyzing the students’ adoption rate of the online examinations related to their academic factors (program of study, GPA, credit earned), socioeconomic status (age, sex, marital status, employment status), and residential factor.

This is an in-depth case study of 1,540 first-year students for the period ranging from the start of the second semester of 2013 to the first semester of 2016 at Bengkulu Regional Office of UT. During the term, quantitative data were set up with ordinal regression to measure the extent to which the adoption rate categories were influenced by such demographic characteristics as academic attainment, personal background, and current residence.

The results showed that while the program of study, grade point average, credits earned, sex, and residence had systematic effects on adopter categories, employment and marital status did not. The highlights of the results were that students with GPA less than or equal to 2.9 were 2.02 times likely to fall into a higher adopter category as compared to those with GPA above 2.9, and students residing in city were 2.50 times more likely to hit the higher levels of adopter category, compared to those residing outside city.

As this is a case study of the students enrolled in the second semester of 2013 and because there has been a rapid change in the way people access information technology, further work should be done, in particular on the sample of students who have enrolled lately.

The institution of open and distance learning (ODL) should accelerate the introduction of new learning resources based on diffusion of innovation modeling.

It is also recommended to especially encourage the new students of ODL to have a feeling of easiness and self-confidence regarding online examinations, and understand their importance. To achieve this objective, regional office staffs can guide the students to try out the online examination in the orientation study activities for new students.

Despite the extensive research on diffusion model for the past decades, this field of study has much more to offer in terms of describing and incorporating such current innovation as an online examination in ODL platforms with which to associate students’ academic and demographic profiles.

The present study is intended to assess the risk factors associated with digital buying. Also aims to design and develop an instrument to assess the digital buyers risk factor score (DBRFS) in light of pandemic.

Present investigation uses a quantitative approach to achieve the stated objectives. The survey instrument for the purpose of assessing risk factors associated with digital buying was developed in two phases. The present study adopts theory of planned behaviour (TPB), built based on the theory of reasoned action (TRA). The data were collected and analysed considering 500 valid responses, sampling unit being digital buyers using social media platforms in tyre-II city of India. The data collection was undertaken between June 2021 and August 2021. The instrument is designed and validated using exploratory factor analysis (EFA) followed by confirmatory factor analysis (CFA).

The present research identified six perceived risk factors that are associated with digital buying; contractual risk, social risk, psychological risk, perceived quality risk, financial risk and time risk. The DBRFS of male is 3.7585, while female is 3.7137. Thus, risk taking by the male and female is at par. For the age group 15–30, DBRFS is 3.6761, while age group 31–45 noted as 3.7889 and for the 46–50 age groups it is measured as 3.9649.

The marketers are expected to have the knowledge about how people responds to the pandemic. The outcome of the research helps to understand consumer behaviour but disentangling consumer’s “black box” is challenging especially during global distress. The present study outcome helps the digital shopkeepers to respond positively to meet the needs of digital buying.

The scale development and to quantify the DBRFS. A deeper understanding of about digital consumers during pandemics will help digital shopkeepers to connect issues related digital buying.

The food industry is continuously developing its online services called food delivery applications (FDAs). This study aims to evaluate FDA's importance–performance and identify strategies to maximize its potential gains from a business partner's perspective.

Data are collected from 208 FDA partners in Indonesia. Importance–performance analysis (IPA) is applied to evaluate the FDA feature and extended the theory of potential gain in customer value (PGCV) to achieve potential gains from FDA business partners.

This study provides a clear and measurable direction for future research to develop FDA performance. Owning customer data, revenue sharing and competitive advantage are the most potential gains from joining the FDA from the business partner perspective.

The respondents are restaurants from the micro, small, and medium enterprises levels. Further research should involve middle to upper level restaurants to discover all business partners' perceptions. This will be very helpful for FDA providers interested in improving the best performance for all their partners.

FDA providers must focus on improving and maintaining the features of owning customer data, revenue sharing, competitive advantage, stable terms and conditions, customer interface, building customer loyalty, online presence, user credit rating, promotion and offers, delivery service and sales enhancement to increase consumer satisfaction and meet the expectations desired by business partners.

This research provides a meaningful theoretical foundation for future work. It extends the theory of PGCV using the value of a partner perspective as a substitute for customer value; hence, the authors call it a potential gain in partner value.

A significant number of studies have been conducted to analyze and understand the relationship between gas emissions and global temperature using conventional statistical approaches. However, these techniques follow assumptions of probabilistic modeling, where results can be associated with large errors. Furthermore, such traditional techniques cannot be applied to imprecise data. The purpose of this paper is to avoid strict assumptions when studying the complex relationships between variables by using the three innovative, up-to-date, statistical modeling tools: adaptive neuro-fuzzy inference systems (ANFIS), artificial neural networks (ANNs) and fuzzy time series models.

These three approaches enabled us to effectively represent the relationship between global carbon dioxide (CO₂) emissions from the energy sector (oil, gas and coal) and the average global temperature increase. Temperature was used in this study (1900-2012). Investigations were conducted into the predictive power and performance of different fuzzy techniques against conventional methods and among the fuzzy techniques themselves.

A performance comparison of the ANFIS model against conventional techniques showed that the root means square error (RMSE) of ANFIS and conventional techniques were found to be 0.1157 and 0.1915, respectively. On the other hand, the correlation coefficients of ANN and the conventional technique were computed to be 0.93 and 0.69, respectively. Furthermore, the fuzzy-based time series analysis of CO₂ emissions and average global temperature using three fuzzy time series modeling techniques (Singh, Abbasov–Mamedova and NFTS) showed that the RMSE of fuzzy and conventional time series models were 110.51 and 1237.10, respectively.

The paper provides more awareness about fuzzy techniques application in CO₂ emissions studies.

These techniques can be extended to other models to assess the impact of CO₂ emission from other sectors.