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This study aims to propose an enhanced eco-driving strategy based on reinforcement learning (RL) to alleviate the mileage anxiety of electric vehicles (EVs) in the connected environment.

In this paper, an enhanced eco-driving control strategy based on an advanced RL algorithm in hybrid action space (EEDC-HRL) is proposed for connected EVs. The EEDC-HRL simultaneously controls longitudinal velocity and lateral lane-changing maneuvers to achieve more potential eco-driving. Moreover, this study redesigns an all-purpose and efficient-training reward function with the aim to achieve energy-saving on the premise of ensuring other driving performance.

To illustrate the performance for the EEDC-HRL, the controlled EV was trained and tested in various traffic flow states. The experimental results demonstrate that the proposed technique can effectively improve energy efficiency, without sacrificing travel efficiency, comfort, safety and lane-changing performance in different traffic flow states.

In light of the aforementioned discussion, the contributions of this paper are two-fold. An enhanced eco-driving strategy based an advanced RL algorithm in hybrid action space (EEDC-HRL) is proposed to jointly optimize longitudinal velocity and lateral lane-changing for connected EVs. A full-scale reward function consisting of multiple sub-rewards with a safety control constraint is redesigned to achieve eco-driving while ensuring other driving performance.

Decision-making is one of the key technologies for self-driving cars. The high dependency of previously existing methods on human driving data or rules makes it difficult to model policies for different driving situations.

In this research, a probabilistic decision-making method based on the Markov decision process (MDP) is proposed to deduce the optimal maneuver automatically in a two-lane highway scenario without using any human data. The decision-making issues in a traffic environment are formulated as the MDP by defining basic elements including states, actions and basic models. Transition and reward models are defined by using a complete prediction model of the surrounding cars. An optimal policy was deduced using a dynamic programing method and evaluated under a two-dimensional simulation environment.

Results show that, at the given scenario, the self-driving car maintained safety and efficiency with the proposed policy.

This paper presents a framework used to derive a driving policy for self-driving cars without relying on any human driving data or rules modeled by hand.

Intrinsic motivation affects job satisfaction and turnover intention. Still, previous motivational studies among real estate brokers (brokers) have primarily focused on extrinsic rewards, leaving intrinsic rewards/motivation practically unexplored. The purpose of this study is therefore to evaluate the role of both satisfaction with intrinsic rewards (SIR) and satisfaction with extrinsic rewards (SER) on job satisfaction and turnover intention among Swedish brokers.

This article is a replication, more precisely an empirical generalization and extension, of Mosquera et al.’s (2020) study conducted among brokers in Portugal. Using a sample of 910 Swedish brokers, the study analyzes a conceptual framework and tests hypotheses by using partial least squares (PLS).

Results indicate that SIR has a very strong impact on job satisfaction, which is not the case in the Portuguese sample. On the other hand, SER does not have an impact on job satisfaction, which is the case in the Portuguese sample. SIR does not have an impact on turnover intention in the Swedish sample, whereas SER does. Job satisfaction has twice the positive impact on turnover intention in the Swedish sample compared to the Portuguese. Furthermore, job satisfaction mediates the relationship between SIR/SER and turnover intention.

Findings of this study extend the existing literature of satisfaction with extrinsic and in particular intrinsic rewards on job satisfaction and turnover intention in the context of the brokerage industry. The most interesting difference between the samples is that Swedish brokers display much higher levels of satisfaction with intrinsic rewards. On the other hand, Swedish brokers appear to be less driven by extrinsic rewards, which is not in line with prior studies within brokerage.

Both managers and students planning to become brokers should consider that SIR has a stronger impact on job satisfaction than SER. What are perceived as intrinsic rewards, however, is highly subjective, which is troublesome from a managerial perspective, even more so as SIR is much harder to influence than SER. Given that intrinsic motivation is primarily a consequence of needs fulfillment, screening of applicants for person-job fit ought to increase job satisfaction and reduce turnover given its focus on the congruence between job demands and worker’s needs, respectively, what a job provides and the worker’s needs.

This study contributes to the brokerage research field by indicating that being a broker differs substantially between countries and that intrinsic rewards matter for Swedish brokers.

This research examines what motivates professional truck drivers to engage in eco-driving by linking their self-reports with objective driving scores.

Theory of Planned Behavior (TPB) is illustrated in an embedded, single-case study of a Finnish carrier with 17 of its truck drivers. Data are obtained through in-depth interviews with drivers, their fuel-efficiency scores generated by fleet telematics and a focus group session with the management.

Discrepancies between drivers’ intentions and eco-driving behaviors are illustrated in a two-by-two matrix that classifies drivers into four categories: ideal eco-drivers, wildcards, wannabes and non-eco-drivers. Attitudes, subjective norms and perceived behavioral control are examined for drivers within each category, revealing that drivers’ perceptions did not always align with the reality of their driving.

This study strengthens the utility of TPB through data triangulation while also revealing the theory’s inherent limitations in elucidating the underlying causes of its three antecedents and their impact on the variance in driving behaviors.

Managerial insights are offered to fleet managers and eco-driving solution providers to stipulate the right conditions for drivers to enhance fuel-efficiency outcomes of transport fleets.

This is one of the first studies to give a voice to professional truck drivers about their daily eco-driving practice.

The purpose of this study is to analyze the relationship between citizen participation and the level of trust in government’s decisions and policies; as well as examining the impact of disclosure of information on the level of citizens’ engagement with governments’ projects. In addition, testing the real motives behind Egyptian citizens’ participation in financing national projects. The study is applied to the “New Suez Canal,” which was finished in only one year opposing the three years implementation period suggested by some studies.

The researcher depended on secondary and primary data as well in working on this paper. She used secondary data gathered from scholars and from domestic and international institutions. Then, she conducted a field study and collected data through distributing 384 Likert Scale questionnaires containing 34 self-administered among respondents to test the following: 1. Citizens perceptions regarding the level of trust in government’s decisions and policies. 2. The impact of citizens’ trust on their willingness to participate in governments’ projects. 3. Is ‘public service motivation’ (PSM) behind citizens’ willingness of participating in national projects. 4. Is the ‘high expected profit of Suez Canal Investment Certificates’ behind citizens participation in national projects.

H1 and H2 have been accepted as trust, transparency and citizen participation proved to be important pillars of building a participatory government. Moreover, citizens’ participation in national projects encouraged national and international enterprises to invest in the canal provision. H3 and H4 are accepted and the statistical study revealed dual contradicting results regarding the motive of citizens’ financial participation in the New Suez Canal project. The justification for the contradiction is that right after the 2011 up-rise, Egyptian citizens were overwhelmed with patriot emotions and feelings pushing them to participate in national projects. At the same time this patriot drive was moderated by the “performance-based rewards and citizens” self-interests’ pushed by the Egyptian government (offering a high-interest rate for Suez Canal Certificates at that time). Citizens might be motivated to participate in national projects triggered by many factors: public service drive – patriotism or self-interest.

The study needs further deeper investigation and empirical pieces of evidence to answer the following questions: would different participatory actions result differently in other circumstances? Do individuals’ levels of PSM vary over time? Besides, the researcher needs to find ways to test PSM against various motives such as self-interest, which needs to be confirmed empirically.

The author came up with important recommendations for central government and decision-makers in Egypt and is based upon the study’s statistical results. The most important recommendations were: central government and decision-makers should frame a policy designed to promote citizens’ participation in decision-making drawing on the guidelines for civil participation in political decision-making. Decision-makers in the central government should work local and regional authorities to update and improve local and regional regulations concerning the participation of citizens in local public life and promote a culture of democratic participation shared by communities and local authorities. Performance-based rewards (high-interest rate) are moderating the citizens’ public service motivation (PSM – patriot sense) and citizens may be motivated by different factors such as public service drive – patriotism or self-interest.

The study is tackling an important issue, which is civil participation in political decision-making. It is also discussing promoting cultural awareness regarding the importance of democratic participation shared by communities and local authorities. The study came up with certain findings proving Egyptian civil society’s willingness in participating with the government in national projects; believing in its socio-economic benefits.

Finally, the study is of value, as it could be considered a pilot study representing the outcomes of citizen participation in national projects; in addition, it can be considered as a road map to policymakers. Moreover, the findings provide a set of recommendations and policies for governments and decision-makers to undertake tangible actions to accelerate citizen participation in further projects and decisions and be able to establish a democratic system in developing countries.

This paper aims to optimize and evaluating the performance of the crowd networks through analyzing their information sharing patterns. That is, in a crowd network, the qualities of accomplishing tasks are highly dependent on the effective information sharing among intelligent subjects within the networks. Hence, proposing an adaptive information-sharing approach can help improve the performance of crowd networks on accomplishing tasks that are assigned to them.

This paper first introduces the factors that affect effectiveness of information-sharing pattern: the network topology, the resources owned by intelligent subjects and the degree of information demand. By analyzing the correlation between these factors and the performance of crowd networks, an Adaptive Information Sharing Approach for Crowd Networks (AISCN approach) is proposed. By referring to information needed for accomplishing the historical tasks that are assigned to a crowd network, the AISCN approach can explore the optimized information-sharing pattern based on the predefined composite objective function. The authors implement their approach on two crowd networks including bee colony and supply chain, to prove the effectiveness of the approach.

The shared information among intelligent subjects affects the efficiency of task completion in the crowd network. The factors that can be used to describe the effectiveness of information-sharing patterns include the network topology, the resources owned by intelligent subjects and the degree of information demand. The AISCN approach used heuristic algorithm to solve a composite objective function which takes all these factors into consideration, so that the optimized information-sharing pattern can be obtained.

This paper introduces a set of factors that can be used to describe the correlation between information-sharing pattern and performance of crowd network. By quantifying such correlation based on these factors, this paper proposes an adaptive information-sharing approach which can explore the optimized information-sharing pattern for a variety of crowd networks. As the approach is a data-driven approach that explores the information-sharing pattern based on the network’s performance on historical tasks and network’s characteristics, it is adaptive to the dynamic change (change of incoming tasks, change of network characteristics) of the target crowd network. To ensure the commonality of the information-sharing approach, the proposed approach is not designed for a specific optimization algorithm. In this way, during the implementation of the proposed approach, heuristic algorithms can be chosen according to the complexity of the target crowd network.

Existing literature on algorithmic management practices – defined as autonomous data-driven decision making in people's management by adoption of self-learning algorithms and artificial intelligence – suggests complex relationships with employees' well-being in the workplace. While the use of algorithms can have positive impacts on people-related decisions, they may also adversely influence job autonomy, perceived justice and – as a result – workplace well-being. Literature review revealed a significant gap in empirical research on the nature and direction of these relationships. Therefore the purpose of this paper is to analyse how algorithmic management practices directly influence workplace well-being, as well as investigating its relationships with job autonomy and total rewards practices.

Conceptual model of relationships between algorithmic management practices, job autonomy, total rewards and workplace well-being has been formulated on the basis of literature review. Proposed model has been empirically verified through confirmatory analysis by means of structural equation modelling (SEM CFA) on a sample of 21,869 European organisations, using data collected by Eurofound and Cedefop in 2019, with the focus of investigating the direct and indirect influence of algorithmic management practices on workplace well-being.

This research confirmed a moderate, direct impact of application of algorithmic management practices on workplace well-being. More importantly the authors found out that this approach has an indirect influence, through negative impact on job autonomy and total rewards practices. The authors observed significant variation in the level of influence depending on the size of the organisation, with the decreasing impacts of algorithmic management on well-being and job autonomy for larger entities.

While the influence of algorithmic management on various workplace practices and effects is now widely discussed, the empirical evidence – especially for traditional work contexts, not only gig economy – is highly limited. The study fills this gap and suggests that algorithmic management – understood as an automated decision-making vehicle – might not always lead to better, well-being focused, people management in organisations. Academic studies and practical applications need to account for possible negative consequences of algorithmic management for the workplace well-being, by better reflecting complex nature of relationships between these variables.

The purpose of this paper is to investigate whether organizational support for innovation and informational extrinsic rewards moderate the relationship between intrinsic motivation and innovative work behavior.

Multiple and hierarchical regression analyses based on data from 150 knowledge workers tested the hypotheses for a South African sample.

The results confirmed a positive relationship between intrinsic motivation and innovative work behavior, and found positive relationships between both organizational support for innovation and informational extrinsic rewards and innovative work behavior. While organizational support positively moderated the relationship between intrinsic motivation and innovative work behavior, acting in synergy with intrinsic motivation, informational extrinsic rewards had a negative moderating effect.

When organizations want to encourage knowledge workers to generate, promote and realize innovative ideas, they should create an environment that encourages autonomy, competence and relatedness, with support for creativity and differences of ideas.

The study provides new indications of the interactions of synergistic extrinsic rewards and intrinsic motivation to affect innovative work behavior.

This paper analyses the factors that make fashion-product crowdfunding campaigns successful. The authors argue that crowdfunding is an innovative and functional way of bringing new fashion items to the market. The purpose of this paper is to answer the question whether product innovation, lifecycle and sustainability have a positive effect on the success of fashion crowdfunding campaigns. The findings highlight that the success of the fashion crowdfunding campaigns depends on creators' adherence to the values of the platform which they use to raise capital.

A total of 300 fashion crowdfunding projects running between the 17th of October and the 15th of December 2017 were collected from Kickstarter – the world's largest crowdfunding platform based on reward-based all-or-nothing model. Two-step binomial logistic regression was used to analyse the data.

The model predicted a significant increase in the odds of success for the fashion items crowdfunded during the first-time production, and innovative and environmentally sustainable products with a higher price range of rewards. In line with previous literature, regression analyses predicted a significant effect of the control variables of goal amount (negative) and the number of rewards (positive). Contrary to previous studies, neither the presence of a video nor the campaign length predicted success.

The novel findings of this study contribute to the literature by providing an analysis of success factors of fashion items on crowdfunding platforms. The results show that innovative, environmentally sustainable and higher-priced products produced by early-stage ventures are better welcomed by the audiences.

Loyalty programs (LPs) in a business-to-business (B2B) context have been under-researched when compared to consumer markets. The purpose of this paper is to investigate if and to what extent the loyalty program activity (LPA) based on recency, frequency and monetary framework reflects the effectiveness of a specific LP.

Using the data obtained from 818 business customers enrolled in a LP, logistic regression models are run to find the impact of LPA on the company’s sales.

The results suggest that in a linear LP, the frequency of rewards impacts sales the most, compared to recency and amount of points redeemed. The intensity of a LPA is influencing the expected sales in a company.

The current study is not focused on the redemption patterns and the value of the rewards offered in the program. Limitation of the study only to one country and in a single company does not allow to generalize presented findings.

Companies should focus their efforts on defining the best level of frequency rewards in their LPs. Reward timing should be considered as a factor that influences the change in customer purchasing behavior more than the amount of points accumulated.

The research provides empirical evidence to support the highest influence of frequency of rewards on sales, compared to recency and amount of points redeemed. This is one of the few LP studies conducted in the context of the B2B market.