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The purpose of this paper is to deal with the practical challenge faced by modern logistics enterprises to accurately evaluate driving performance with high computational efficiency under the disturbance of road smoothness and to identify significantly associated performance influence factors.

The authors cooperate with a logistics server (G7) and establish a driving grading system by constructing real-time inertial navigation data-enabled indicators for both driving behaviour (times of aggressive speed change and times of lane change) and road smoothness (average speed and average vibration times of the vehicle body).

The developed driving grading system demonstrates highly accurate evaluations in practical use. Data analytics on the constructed indicators prove the significances of both driving behaviour heterogeneity and the road smoothness effect on objective driving grading. The methodologies are validated with real-life tests on different types of vehicles, and are confirmed to be quite effective in practical tests with 95% accuracy according to prior benchmarks. Data analytics based on the grading system validate the hypotheses of the driving fatigue effect, daily traffic periods impact and transition effect. In addition, the authors empirically distinguish the impact strength of external factors (driving time, rainfall and humidity, wind speed, and air quality) on driving performance.

This study has good potential for providing objective driving grading as required by the modern logistics industry to improve transparent management efficiency with real-time vehicle data.

This study contributes to the existing research by comprehensively measuring both road smoothness and driving performance in the driving grading system in the modern logistics industry.

The extensive public financing for infrastructure guided by the state-led development policy has crowded out private sector participation for infrastructure development in Ethiopia. Although public-private partnership (PPP) has been used by many countries, the Government of Ethiopia has started to adopt it for major infrastructure development recently. Thus, it is important to investigate the reasons that motivated the government to adopt the PPP model to provide insights to the sector players. This paper aims to explore the driving factors for adopting PPP for infrastructure development focusing on the energy sector of Ethiopia and compared it with other countries.

The study used a comprehensive literature review of previous research outcomes and a purposively sampled questionnaire survey of professionals in Ethiopia.

The results of the study revealed that the five top driving factors perceived by the experts for adopting PPP in the energy sector of Ethiopia include the private sector has the ability to raise funds for project, facilitate creative and innovative approaches, save time in delivering the project, accelerate project development and private sector possess better mobility. The comparison of the driving factors with other countries also suggests that each county has its own reason and preference for adopting the PPP model.

The research result contributes to the development of PPP in Ethiopia and other developing countries at the early stage of PPP implementation. It also provides information to policymakers, sponsors, financiers and developers of PPP projects to understand the government's motivations to implement PPP in Ethiopia.

The purpose of this paper is to develop and demonstrate a driving factors-based approach for identifying and ranking performance indicators in integrated sugarcane supply and processing systems (ISSPSs) on an on-going basis.

The research included a literature review, development of an approach, testing of the approach in four ISSPSs and checking the tests’ results for consistency with Southern Africa sugar industry benchmarks and external knowledge of the four ISSPSs.

The research offers a systematic approach for identifying and ranking performance indicators based on existing driving factors in ISSPSs. Results obtained from the application of the approach in four ISSPSs are consistent with industry benchmarks and external knowledge of the ISSPSs.

The approach was tested in only four ISSPSs. It is recommended that the approach should be tested in other complex systems to further validate its effectiveness. It is further recommended that the approach should be systematically compared with existing approaches that are used for identifying and ranking performance indicators.

This research is of academic value and of practical value to practitioners in ISSPSs. The research blends knowledge from network theory and cause-and-effect analysis to come up with a systematic approach for identifying and ranking performance indicators in ISSPSs on an on-going basis. Further, the approach identifies and ranks performance indicators as part of one data set. This approach has never, to the authors’ knowledge, been used in agro-industry before.

Given the increasing importance of engineering insurance, it is still unclear which specific factors can enhance the role of engineering insurance as a risk transfer tool. This study aims to propose a hybrid approach to identify and analyze the key determinants influencing the consumption of engineering insurance in mainland China.

The empirical analysis utilizes provincial data from mainland China from 2008 to 2019. The research framework is a novel amalgamation of the generalized method of moments (GMM) model, the quantile regression (QR) technique and the random forest (RF) algorithm. This innovative hybrid approach provides a comprehensive exploration of the driving factors while also allowing for an examination across different quantiles of insurance consumption.

The study identifies several driving factors that significantly impact engineering insurance consumption. Income, financial development, inflation, price, risk aversion, market structure and the social security system have a positive and significant influence on engineering insurance consumption. However, urbanization exhibits a negative and significant effect on the consumption of engineering insurance. QR techniques reveal variations in the effects of these driving factors across different levels of engineering insurance consumption.

This study extends the research on insurance consumption to the domain of the engineering business, making theoretical and practical contributions. The findings enrich the knowledge of insurance consumption by identifying the driving factors specific to engineering insurance for the first time. The research framework provides a novel and useful tool for examining the determinants of insurance consumption. Furthermore, the study offers insights into the engineering insurance market and its implications for policymakers and market participants.

The nature of knowledge and the way it is assimilated do play a vital role in influencing knowledge sharing tendencies. These specific factors coupled with the ineffectiveness of the intrinsic knowledge management system point towards an inherent knowledge hiding tendency that exists within every organization. Knowledge hiding is established as an intentional attempt to hide knowledge when it is requested. In the recent past, scholars have investigated the factors causing hiding of the task-related knowledge. However, there is still no clear understanding of the strategic factors that lead to knowledge hiding in organizations and which of these factors are the most critical ones. The purpose of this paper is to identify the most critical strategic factors that cause knowledge hiding in different contexts.

This research study tries to identify these factors from the literature, corroborate it with industry experts and model the same with the aid of total interpretive structural modelling. This is followed by Matrice d'Impacts Croises Multiplication Appliquée a un Classement and sensitivity analysis, which determines the unique driving factors and their powers, which vary based on industry sectors and years of work experience.

This study found out that knowledge hiding behaviour is more pronounced in the initial years of an individual within the organization and similarly in the final years of an individual within the organization. In a period in between both these, there are no critical driving factors that infuse knowledge hiding.

This is one of the first comprehensive research studies that unravel the dynamic nature of the strategic factors engendering knowledge hiding across two different dimensions, namely, industry sectors and work-experience of individuals. This study categorically aims to aid the management in bringing out necessary interventions to curb the menace of knowledge hiding.

In the realm of energy behaviour studies, very little research has been done to understand industrial energy behaviour (IEB) that influences the willingness to adopt (WTA) energy-efficient measures. Most of the studies on energy behaviour were focused on the residential and commercial sectors where the behaviour under investigation was under volitional control, that is, where people believe that they can execute the behaviour whenever they are willing to do so. The purpose of this paper is to examine the factors influencing the industry’s intentions and behaviour that leads to enhanced adoption of energy efficiency measures recommended through energy audits. In particular, this paper aims to extend the existing behaviour intention models using the total interpretive structural modelling (TISM) method and expert feedback to develop an IEB model

TISM technique was used to determine the relationship between different elements of the behaviour. Responses were collected from experts in the field of energy efficiency to understand the relationship between identified factors, their driving power and dependency.

The results show that values, socialisation and leadership of individuals are the key driving factors in deciding the individual energy behaviour. WTA energy-saving measures recommended by an energy auditor are found to be highly dependent on the organisational policies such as energy policy, delegation of power to energy manager and life cycle cost evaluation in purchase policy.

This study has a few limitations that warrant consideration in future research. First, the data came from a small sample of energy experts based on a convenience sample of Indian experts. This limits the generalizability of the results. Individual and organizational behaviour analysed in this study looked into a few select characteristics, derived from the literature review and expert feedback, which may pose questions about the standard for behaviours in different industries.

Reasons for non-adoption of energy audit recommendations are rarely shared by the industries and the analysis of individual and organisational behaviour through structured questionnaire and surveys have serious limitations. Under this circumstance, collecting expert feedback and using the TISM method to build an IEB model can help to build strategies to enhance the adoption of energy-efficient measures.

Various policy level interventions and regulatory measures in the energy field, adopted across the globe, are found unsuccessful in narrowing the energy-efficiency gap, reducing the greenhouse gas (GHG) emissions and global warming. Understanding the key driving factors can help develop effective intervention strategies to improve energy efficiency and reduce GHG emissions.

The industry energy behaviour model with driving, linking and dependent factors and factor hierarchy is a novel contribution to the theory of organisational behaviour. The model takes into consideration both the individual and organisational factors where the decision-making is not strictly under volitional control. Understanding the key driving factor of behaviour can help design an effective intervention strategy that addresses the barriers to energy efficiency improvement. The results imply that it is important to carry out post energy audit studies to understand the implementation rate of recommendations and also the individual and organisational factors that influence the WTA energy-saving measures.

The purpose of this paper is to demonstrate application of the Taguchi method‐based conceptual signal‐to‐noise (S/N) approach and Pareto analysis of variance (ANOVA) to determine optimum level of three important factors related to mobile phone conversation during driving, namely time of drive (day or night), distance between cars, and mobile phone call duration that minimizes drivers' reaction time (RT) in braking response.

Three levels of each factor based on the experience, are considered in the present study. The design of experiment, in the form of an L₁₈ orthogonal array, as proposed by Genichi Taguchi is used to conduct 18 experiments. A total of 27 young male subjects participated in the experimental study. The experimental task involved pressing the brake pedal of the car by the driver upon seeing another car in front while the driver is talking on a mobile phone. Drivers' performance, measured in terms of RT in braking response, is recorded. Conceptual S/N ratio and Pareto ANOVA are employed to investigate the drivers' performance.

Results show that within the test range, drivers' performance, i.e. RT in braking response is optimum (minimum) when they performed the driving task at day time, distance between cars is long (20 meters), and mobile phone call duration is short (30 seconds). Mobile call duration is found to be the dominant parameter with a percentage contribution ratio of 41.882 percent towards the laid down objective followed by time of driving, 32.003 percent and distance between cars, 1.353 percent.

This paper provides valuable information to the drivers with respect to the use of optimum level of these factors in order to minimize their RT in braking response.

To the best of the authors' knowledge, no study has been conducted in the past to investigate the effect of these factors on the performance of drivers when they use mobile phones for conversation during driving. In addition, no attempt has yet been made to find the optimal level of these factors from drivers' performance in braking response view point. This paper is an original research work of authors and in the opinion it carries significantly important values as it provides new information to the persons who talk on mobile phones during driving.

The purpose of this paper is to explore key factors driving four types of brand relationships, which will explain the reasons why different relationships can form in theory and direct brand managers to build brand relationships in practice.

A sample of 702 data was collected through four branch scales. The empirical methods of exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were used in turn to extract the key factors driving every kind of brand relationships.

The conclusion is that the established instrument relationships are driven by three factors which are: sociogroup pressure, condition restriction and saving the existing; the established emotion relationships are driven by four factors which are company reputation, approval of geography, approval of authority and word of mouth; the acquired instrument relationships are driven by three factors which are low price, brand homogeneity and attempt on new products: and the acquired emotion relationships are driven by six factors which are brand meaning, the staffs' service, marketing promotion, product design, product value and brand element.

There were three limitations in this research. First, the limitation of the sample structure decided this research as an exploratory one. Second, this research only adopted a static perspective although a relationship is a dynamic concept. Third, a few items were not drawn up appropriately so that the fitness of a few models was not perfect.

The formation mechanisms of different brand relationships have not been entirely explored in the former literatures. Based on a new sortation theory of four brand relationships in China, this research exploratorily put forward the completed key factors driving every kind of relationships.

The purpose of this study is to identify driving factors and a quantitative model for implementing public-private partnership (PPP) projects in Ethiopia as a case study in emerging economies.

A review of the literature and semi-structured interviews were carried out to identify driving factors affecting the implementation of PPP projects in the Ethiopian context. Data were collected through a questionnaire survey within three months, with 59 validated responses; mean score technique and factor analysis were conducted. The fuzzy synthetic evaluation (FSE) method was applied to develop a driving index (DI) for implementing infrastructure PPP projects. Finally, a comparative analysis of top-five drivers was conducted between four emerging economies.

Mean values show that all driving variables are important. Through factor analysis, 22 identified driving variables were grouped into six factors, namely, benefit for public and private sectors, attention of private sector, social development, cost reduction, management ability of public sector and ability of private sector. The FSE method constructs a DI and shows that benefit for public and private sectors is the most crucial factor for PPP implementation in the context of Ethiopia. Apart from this, most driving forces for adopting PPP projects in these countries related to financial problems.

This study is one of the first integrate driving factors for PPP implementation. The index provides the decision-makers with a comprehensive tool to assess the needs of PPP implementation.

The purpose of this paper is to identify the effect of people’s personality on driving behavior and traffic accidents and violations in the province of South Sulawesi.

This research was conducted in order to determine the moderating effects of age on the relationship between personality variables, driving behavior and driving outcomes. The research was conducted over two years. For the first year of this study, research was conducted in the city of Makassar, the capital of the South Sulawesi province, which has the highest volume of accidents compared to other districts/cities in South Sulawesi. The approach used in conducting the data analysis was a quantitative approach; the inferential statistical analysis method of analysis used to test the hypothesis of the research was structural equation modeling.

The results of the analysis show that age is a moderating variable in the relationship between personality, driving behavior and driving outcomes. The higher the age, the stronger the influence of personality on driving behavior and driving outcomes, and the stronger the effect of driving behavior on driving outcomes.

Originality for this paper is shown as follows: using age on personality has a moderating effect on the relationship between driving behavior and driving outcomes; and the research would implicate driving behavior and inclined factors from the eastern part of Indonesia, since most research works were conducted in the western part of Indonesia and they hardly considered the moderating effect of age.