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Online ride-hailing platforms match drivers with passengers by receiving ride requests from passengers and forwarding them to the nearest driver. In this context, the low acceptance rate of offers by drivers leads to friction in the process of driver and passenger matching. What policies by the platform may increase the acceptance rate and by how much? What factors influence drivers' decisions to accept or reject offers and how much? Are drivers more likely to turn down a ride offer because they know that by rejecting it, they can quickly receive another offer, or do they reject offers due to the availability of outside options? This paper aims to answer such questions using a novel dataset from Tapsi, a ride-hailing platform located in Iran.

The authors specify a structural discrete dynamic programming model to evaluate how drivers decide whether to accept or reject a ride offer. Using this model, the authors quantitatively measure the effect of different policies that increase the acceptance rate. In this model, drivers compare the value of each ride offer with the value of outside options and the value of waiting for better offers before making a decision. The authors use the simulated method of moments (SMM) method to match the dynamic model with the data from Tapsi and estimate the model's parameters.

The authors find that the low driver acceptance rate is mainly due to the availability of a variety of outside options. Therefore, even hiding information from or imposing fines on drivers who reject ride offers cannot motivate drivers to accept more offers and does not affect drivers' welfare by a large amount. The results show that by hiding the information, the average acceptance rate increases by about 1.81 percentage point; while, it is 4.5 percentage points if there were no outside options. Moreover, results show that the imposition of a 10-min delay penalty increases acceptance rate by only 0.07 percentage points.

To answer the questions of the paper, the authors use a novel and new dataset from a ride-hailing company, Tapsi, located in a Middle East country, Iran and specify a structural discrete dynamic programming model to evaluate how drivers decide whether to accept or reject a ride offer. Using this model, the authors quantitatively measure the effect of different policies that could potentially increase the acceptance rate.

The purpose of this paper is to look at two particular aspects of open access megajournals, a new type of scholarly journals. Such journals only review for scientific soundness and leave the judgment of scientific impact to the readers. The two leading journals currently each publish more than 20,000 articles per year. The publishing speed of such journals and acceptance rates of such journals are the topics of the study.

Submission, acceptance and publication dates for a sample of articles in 12 megajournals were manually extracted from the articles. Information about acceptance rates was obtained using web searches of journal home pages, editorials, blogs, etc.

The time from submission to publication varies a lot, with engineering megajournals publishing much more rapidly. But on average it takes almost half a year to get published, particularly in the high-volume biomedical journals. As some of the journals have grown in publication volume, the average review time has increased by almost two months. Acceptance rates have slightly decreased over the past five years, and are now in the range of 50–55 percent.

This is the first empirical study of how long it takes to get published in megajournals and it highlights a clear increase of around two months in publishing. Currently, the review process in the biomedical megajournals takes as long as in regular more selective journals in the same fields. Possible explanations could be increasing difficulties in finding willing and motivated reviewers and in a higher share of submissions from developing countries.

Of the three ingredients required for a commercially successful innovation to occur – the technological feasibility, the entrepreneur, and the customer need/want – the most important is customer need. The notion of customer acceptance of a technological innovation prior to its diffusion has largely been neglected in research on marketing. However, marketing personnel and the entire business community need to understand the reasons behind customer acceptance or rejection in order to become more efficient in research and development and to contribute to the competitiveness and profitability of the firm. Examines the determinants of diffusion; those factors which determine whether or not an innovation is accepted, with special note of determinants of customer acceptance of an innovation.

This paper aims to investigate the relation between the creditworthiness of US institutions of higher education and their student selectivity (i.e. demand and quality).

The authors study whether the impact of student selectivity differs across public vs private universities; across the credit quality of the given public university’s state; and across the level of state appropriations for the given public university.

The authors find that student quality and demand measures are significantly associated with their corresponding institution’s creditworthiness, especially for private universities.

For public universities the association is weak and, contrary to the expectations, does not depend on the state credit quality or level of state funding. The findings are robust to the inclusion of control variables.

The purpose of this paper is to compare the acceptance ratings and drivers of liking and disliking attributes of aseptic-packaged cooked rice by consumers, researchers and experts.

Descriptive analysis (DA) was conducted using trained panelists. Acceptability was measured by consumers, researchers and experts. The results of DA and acceptability were analyzed using partial least square regression.

There was no strong relationship among the three groups in their rating patterns for the samples (r=−0.342-0.445). The liking factors for each group were as follows: consumers (rice cake flavor and moisture), researchers (wet wood flavor and whiteness) and experts (wet wood flavor and size of rice). The disliking factors for each group were as follows: consumers (wet wood flavor and brown particle), researchers (moisture) and experts (old rice aroma). The consumers, researchers and experts seemed to have different acceptances and key descriptive attributes for aseptic-packaged cooked rice.

The consensus by researchers during the product development process required caution with regard to the fact that the evaluation by the researchers could be different from what consumers or experts prefer.

Setting-up in-house panelists group would be minimized the discrepancy between consumers and researches.

This study contributes to understanding of the acceptability by food researchers and comparing to consumers and experts for the first time in sensory field.

The Iranian construction industry has been grappling with numerous problems in recent years, including rework, high costs and design errors. Engineers in this field have always highlighted the use of modern technological methods of construction to improve quality and productivity and reduce time and cost. One of these technologies is the so-called building information modeling (BIM), which has been very difficult to adopt and implement in Iran. The purpose of this study is to propose a systemic and holistic model to analyze the dynamics of adoption and implementation of BIM in this country. The purpose of this paper is to understand the dynamics of BIM acceptance to identify the most effective policy to maximize it in the Iranian manufacturing industry.

A two-stage methodology has been developed to achieve the purpose of the research. In the first stage, a technology acceptance model for BIM acceptance was developed using the grounded theory (GT) method. This conceptual model provides a holistic basis for building a simulation model. Thus, in the second stage, we used the dynamics system methodology to extract a dynamic model from the conceptual one. This dynamic model can simulate different policies and may be used to evaluate their respective effectiveness.

In this study, using the GT method, we obtained 510 primary codes, 118 secondary codes, 50 concepts and 17 categories. After determining the relationships between categories through axial coding, we reached a conceptual model based on selective coding. Mention some of the variables of the conceptual model. Awareness, security, perceived usefulness and perceived ease of use are some of the most important variables of this model. In the next part, this conceptual model was run using system dynamics and, thus, turned into a causal model in which all the effective variables on BIM technology and their relationships with each other are specified. The stock and flow diagram of the problem and its related equations were presented. To improve the model and solve the problem, we examined the four policies as four future scenarios on the model: continuing the status quo, development of specialist workforce training, bolstering governmental support and increasing awareness via advertisement within. The simulation results showed that government support is the most effective policy for maximizing BIM acceptance in Iran.

In addition to enumerating all the factors affecting BIM technology, this paper proposes a systemic model that provides an accurate and comprehensive view of the acceptance of this technology. In this regard, by introducing feedback loops, as well as reinforcing and balancing factors versus factors causing stasis, the model offers a much deeper insight into mechanisms associated with BIM development and its barriers. Therefore, this study provides a very useful perspective and basis for policy-makers and all stakeholders to accept and implement BIM technology. The findings of this study can lead to more accurate policy-making, removal of acceptance barriers, promotion of incentives, and consequently more effective acceptance of BIM technology.

In this study, a new mixed research method was used. The innovation of our study lies in its simultaneous use of GT method to construct an accurate and holistic model and applying the system dynamics methodology to build a holistic and systemic model of the BIM acceptance problem. This research also provides a suitable standard and tool for studying BIM technology in developing countries.

The roles recommendation systems play in health care have become crucial in achieving effective care and in meeting the needs of modern care giving. As a result, efforts have been geared toward using recommendation systems in the management of chronic diseases. Effectiveness of these systems is determined by evaluation following implementation and before deployment, using certain metrics and criteria. The purpose of this study is to ascertain whether consideration of criteria during the design of a recommendation system can increase acceptance and usefulness of the recommendation system.

Using survey-style requirements gathering method, the specific health and technology needs of people living with chronic diseases were gathered. The result was analyzed using quantitative method. Sets of harmonized criteria and metrics were used along with requirements gathered from stakeholders to establish relationship among the criteria and the requirements. A matching matrix was used to isolate requirements for prioritization. These requirements were used in the design of a mobile app.

Matching criteria against requirements highlights three possible matches, namely, exact, inferential and zero matches. In any of these matches, no requirement was discarded. This allows priority features of the system to be isolated and accorded high priority during the design. This study highlights the possibility of increasing the acceptance rate and usefulness of a recommendation system by using metrics and criteria as a guide during the design process of recommendation systems in health care. This approach was applied in the design of a mobile app called Recommendations Sharing Community for Aged and Chronically Ill People. The result has shown that with this method, it is possible to increase acceptance rate, robustness and usefulness of the product.

Inability to know the evaluation criteria beforehand, inability to do functional analysis of requirements, lack of well-defined requirements and often poor cooperation from people living with chronic diseases during requirements gathering for fear of stigmatization, confidentiality and privacy breaches are possible limitations to this study.

The result has shown that with this method, it is possible to isolate more important features of the system and use them during the design process, thereby speeding up the design and increasing acceptance rate, robustness and usefulness of the system. It also helps to see in advance the likely features of the system that will enhance its usefulness and acceptance, thereby increasing the confidence of the developers in their ability to deliver a system that will meet users’ needs. As a result, developers know beforehand where to concentrate their efforts during system development to ascertain the possibility of increasing usefulness and acceptance rate of a recommendation system. In addition, it will also save time and cost.

This paper demonstrates originality by highlighting and testing the possibility of using evaluation criteria and metrics during the design of a recommender system with a view to increasing acceptance and enhancing usefulness. It also shows the possibility of using the metrics and criteria in system’s development process for an exercise other than evaluation.

University rankings and metrics have become an increasingly prominent basis of student decisions, generalized university reputation, and the resources university’s attract. We review the history of metrics in higher education and scholarship about the influence of ranking on the position and strategic behavior of universities and students. Most quantitative analyses on this topic estimate the influence of change in university rank on performance. These studies consistently identify a small, short-lived influence of rank shift on selectivity (e.g., one rank position corresponds to ≤1% more student applicants), comparable to ranking effects documented in other domains. This understates the larger system-level impact of metrification on universities, students, and the professions that surround them. We explore one system-level transformation likely influenced by the rise of rankings. Recent years have witnessed the rise of enrollment management and independent educational consultation. We illustrate a plausible pathway from ranking to this transformation: In an effort to improve rankings, universities solicit more applications from students to reduce their acceptance rate. Lower acceptance rates lead to more uncertainty for students about acceptance, leading them to apply to more schools, which decreases the probability that accepted students will attend. This leads to greater uncertainty about enrollment for students and universities and generates demand for new services to manage it. Because these and other system-level transformations are not as cleanly measured as rank position and performance, they have not received the same treatment or modeling attention in higher education scholarship, despite their importance for understanding and influencing education policy.

The proliferation of ride-hailing businesses brings significant considerations for improving the driver's operational performance. Informed by the literature on sharing economy, general deterrence theory and protection motivation theory this research investigates the behavioral factors impacting ride-hailing drivers' operational performance.

The authors empirically test the antecedents impacting a ride-hailing driver's operational performance using an online survey dataset comprising 513 ride-hailing drivers working for Uber and Lyft in the United States.

Ride-hailing drivers' intention to comply with the ride-hailing company guidelines results in better operational performance for the driver. Moreover, drivers believe that ride-hailing companies have effective penalties to deter drivers from violating company guidelines. However, drivers also believe that the chances of being caught while ignoring the company guidelines are low.

The results of this research support the decision-making processes of ride-hailing company managers and offer insights on how managers can enhance the operational performance of their drivers.

This study provides unique contributions to emerging research at the intersection of peer-to-peer asset sharing, behavioral studies and technology management. This research is one of the first to explore the role of behavioral factors such as coping mechanisms on the operational performance of sharing economy workers.