Model Of Drivers Research Articles

Casually cynical or trapped? Exploring gig workers’ reactions to psychological contract violation

PurposeDrawing on psychological contract (PC) theory and platform labor research, the purpose of our study was to explore gig workers’ reactions to perceived PC violation. Our study was set against the backdrop of the COVID-19 pandemic, which brought workplace health and safety issues into much sharper focus, even in nonstandard employment arrangements like gig work.Design/methodology/approachThis study employed a mixed-methods design. In Study 1, we tested a conceptual model of US-based ride-hail drivers’ (n = 202) affective and cognitive reactions to Uber’s (lack of) commitment to safe working conditions. In Study 2, we conducted interviews with 32 platform workers to further explore an unexpected finding from Study 1.FindingsIn Study 1, we found that drivers’ perceptions of PC violation were related to decreased trust in Uber and higher intentions to leave this line of work; however, cynicism toward Uber only predicted withdrawal intentions for those drivers who did not believe that they had job alternatives available outside of gig work. We explored this further in Study 2, where we found that workers with low economic dependence on gig work could afford to be casually cynical toward the platform, while high-dependence workers felt “trapped” in this line of work.Originality/valueWe contribute to the social/relational theoretical approach to gig work more broadly and to the literature on PC in platform work more specifically. We also add to the emerging literature on how economic dependence shapes workers’ experience of platform work. Our findings around low-dependence gig workers experiencing a more indifferent form of cynicism – which we have termed casual cynicism – highlight the importance of treating the context of gig work as unique, not merely an extension of traditional management research.

Human-robot collaborative decision method of hexapod robot based on prior knowledge and negotiation strategy

Against the unstructured terrain environment, human drivers often rely on remote operating patterns to accomplish tasks with robots. With the gradual improvement in robot intelligence, a key problem that urgently requires attention is how to bridge cognitive and perceptual differences between human and robot in collaborative decision-making processes. Particularly effective resolution of differences in decision outcomes is crucial. Therefore, this paper utilizes the decision logic process and prior knowledge model of human drivers to form a robot's decision framework, narrowing the cognitive differences between human and robot. Besides, embedding the human-robot-environment features that affect the decision-making process as prior knowledge into the robot's decision-making system can effectively reduce perceptual gaps. Under the same decision framework, a negotiation strategy is proposed for forming a unified decision outcome between human and robot by finding an optimal concession rate. This study utilizes a hexapod robot simulated driving platform to gather experimental data and develop prior knowledge for robots. It also leverages virtual reality equipment and visual augmentation technologies to establish a remote human-robot collaborative decision-making experimental system. The experimental results conducted in complex obstacle terrain demonstrate the effectiveness of adopting the negotiation strategy for human-robot collaborative decision-making. Compared with the individual human or robot decision, the human-robot collaborative decision method significantly enhances robot stability, reduces energy consumption, and minimizes collisions.

Model of Motivational Drivers of Online Purchase Intention: Mediating Role of Consumers Commitment

Over the years, marketing has substantially developed both as an academic field and as a management activity. Notwithstanding the heightened attention, there is a lack of adequate research on the online purchasing intentions for fashion products. This article aims to develop a model of the motivational drivers that affect consumers' online purchase intentions for fashion products in Nigeria. The study posited a conceptual framework linking motivating drivers to online purchase intentions for fashion products, mediated by consumer commitment. The study employs secondary data to guide the study. The proposed conceptual model is developed as a reflecting formative type II construct. Prospective researchers are urged to undertake empirical investigations grounded in the conceptual framework presented in this study. The limitations identified in this study will stimulate debates regarding the need to improve comprehension of the drivers influencing online purchase intentions for fashion products. The study will contribute to the existing literature by identifying the drivers influencing online fashion product purchases.

Quantification of enrichment processes in throughfall and stemflow in a mixed temperate forest

AbstractForest ecosystems depend on throughfall and stemflow fluxes for both water and nutrient input. Spatial and temporal variability of throughfall and stemflow fluxes are large and differ between tree species. The nutrient fluxes that accompany throughfall and stemflow are affected by climate, precipitation intensity, the seasonality of dry deposition, and canopy exchange processes. The interdependence of these factors makes it challenging to quantify changes in throughfall and stemflow amounts as well as their nutrient content. Here, we provide observation‐based evidence from 3.5 years of data collection with 222 rainfall events, of the seasonal variability of throughfall and stemflow magnitude and ion concentrations under a beech (Fagus silvatica) and spruce (Picea abies) tree. Interception and canopy cover were seasonally variable, average annual interception was 53% below beech, 61% below spruce and 68% below young spruce canopies. Further, we assess seasonality of ionic nutrients such as NH4 and NO3 as well as Mg, Ca and K and their dependence on both dry deposition and canopy exchange. Throughfall and stemflow were enriched compared to precipitation, with large differences between ions and different months. Antecedent precipitation was a main control on throughfall and stemflow enrichment. We developed a conceptual model of the potential drivers of throughfall and stemflow enrichment based on our observations. While NH4 and NO3 enrichment are likely dominated by dry deposition and dew and fog accumulation, Mg, Ca and K were additionally affected by canopy exchange. Observation based studies such as this one are needed to understand precipitation and nutrient partitioning across forests, which enables the prediction of how changes in climate and forest composition will affect local hydrology and nutrient inputs into forest ecosystems.

Probabilistic modeling and simulation of drivers’ lane selection and its impact on highway traffic using fuzzy-based decision-making

Traffic on highways has increased significantly in the past few years. Consequently, this has caused delays for the drivers in reaching their final destination and increased the highway’s congestion level. Many options have been proposed to ease these issues. In this paper, a model of the highway drivers’ population was built based on several factors, including the behavioral patterns of the drivers, like drivers’ time flexibility to reach the destination, their carpool eligibility, and their tolerance to pay the toll price, in addition to the traffic information from the system. A fuzzy logic decision-making model is presented to emulate how drivers would choose the lane to use based on the aforementioned factors and the current congestion levels of all the lanes on the highway. The presented model, along with the simulation results from applying the model to different simulation scenarios, show the usefulness of such a model in predicting an optimal toll value. Such optimal value would reduce congestion on the highway at one end while maximizing the revenue for the toll company.

Structural Analysis of Pre-hazard Preparedness Criteria in Iranian Large Cities

Studies have shown that Iranian large cities have experienced the most casualties and damages from natural hazards in 2010–2020 due to structural reasons. This research aims to extract, classify and determine the final model of drivers affecting the preparedness of these cities against natural hazards. The criteria were extracted by studying records and using the Delphi model’s opinions of 150 experts and city managers in 10 large cities. The combined output was 8 criteria and 70 sub-criteria that were assorted by considering the particular conditions of each study area. To obtain a multi-dimensional and comprehensive model, PROMETHEE and GAIA as structural methods have been used. PROMETHEE findings indicated that the most effective criteria for preparedness in large cities of Iran against natural hazards are institutional-managerial and social-citizenship. Also, based on GAIA VISUAL results, the comprehensive disaster preparedness plan and hazard mapping were identified as vital drivers and prerequisites of hazard preparedness. The output of GAIA has also shown that regarding the challenges of local government financing, the economic criteria (hazard-based investment, emergency budget and business continuity training) were the main facilitators and accelerators of the preparedness of Iranian large cities against natural hazards.

Brands as drivers of social media fatigue and its effects on users’ disengagement: the perspective of young consumers

PurposeSocial media has become an inescapable part of our lives. However, recent research suggests that excessive use of social media may lead to fatigue and users’ disengagement. This study aims to examine which brand-related factors contribute to social media fatigue (SMF) and its subsequent role on driving lurking behaviors, particularly among young consumers.Design/methodology/approachBased on survey data from 282 young users of social media, a holistic model of brand-related drivers and outcomes of SMF was tested, emphasizing the contribution of brands’ social media presence to users’ disengagement.FindingsResearch shows that branded content overload and irrelevance, as well as branded ads intrusiveness significantly impact SMF, which in turn plays a mediating role between brand-related drivers and lurking behaviors. The authors further conclude that the impact of SMF on lurking is stronger for users who follow a larger set of brands.Originality/valueThe study contributes to social media research by addressing its “dark side” and empirically validating the role of brands’ social media presence in developing young users’ fatigue and disengagement. The study further adds to the scant literature on SMF, which was mostly developed outside the branding field. Research also provides valuable insights to brands on how to improve their social media performance.

Psychological Factors Affecting Drive-by-Lane Behavior at Lane Offset Intersections: Analysis Based on Extended Theory of Planned Behavior in a Chinese Sample

Traffic conflict commonly occurs in intersections with offset lane lines because of mismatched choices of entrance and exit lanes. This conflict drives down overall traffic efficiency and can cause crashes. At such intersections, the driver’s lane choice behavior seriously affects the traffic system. The theory of planned behavior (TPB) was used in this study to excavate the impact mechanism of drive-by-lane behavior at offset intersections. A scale incorporating TPB constructs and additional variables (risk perception [RP], traffic environment [TE], optimization measures [OM], and driving style [DS]) was developed to collect empirical data with 557 valid samples in China. A structural equation model of drivers’ drive-by-lane behavior was established to explore the causal relationship between behavior and influencing factors, as well as the interrelationships between TPB variables. TE, behavior intention (BI), subjective norms (SN), and RP show the most significant effects on drive-by-lane behavior. DS, perceived behavior control, attitude (ATT), and OM have little impact on drive-by-lane behavior. BI, TE, RP, and DS have significant, direct effects on drive-by-lane behavior. The TE, OM, and DS variables significantly affect drive-by-lane behavior through mediators. RP, BI, and ATT mediate the influence of other variables as well as affecting drive-by-lane behavior. We put forward suggestions to improve driving behavior at offset intersections including changing external factors, education, and training. The direct and indirect relationships between the influencing factors and drive-by-lane behavior are discussed in this paper to provide technical and reference support for future research on managing irregular urban traffic intersections.

Identifying the critical oncogenic mechanism of LDHA based on a prognostic model of T-cell synthetic drivers

BackgroundDespite its early success, immunotherapy focused on removing T-cell inhibition does not achieve the desired effect in most patients. New strategies that target antigen-driven T-cell activation are needed to improve immunotherapy outcomes. However, a comprehensive analysis of synthetic drivers of T-cells is greatly lacking in lung adenocarcinoma (LUAD) and other types of tumors. MethodsWe comprehensively evaluated the patterns of LUAD patients based on T -cell synthetic drivers by unsupervised clustering analysis. A risk model was constructed using Lasso Cox regression analysis. The predicted survival and immunotherapy efficacy of the model was validated by independent cohorts. Finally, single-cell sequencing analysis, and a series of in vitro experiments were conducted to explore the role of lactate dehydrogenase A (LDHA) in the malignant progression of LUAD. ResultsPatients in the high-risk group were characterized by survival disadvantage, a “cold” immune phenotype, and by not having benefitted from immunotherapy. LDHA was shown to promote LUAD cell proliferation, cell cycle, invasion, and migration. Secondly, we found that LDHA induced NF-κB pathway activation, tyrosine kinase inhibitor resistance and immunosuppressant microenvironment. Finally, LDHA was found to be highly expressed in fibroblasts, which may be involved in promoting TKI resistance and mediating the immune escape. ConclusionThis study revealed that the T-cell synthetic driver-associated prognostic model developed herein significantly predicted prognosis and immunotherapy efficacy in LUAD. We further investigated the role of LDHA in the malignant phenotype of tumor cells and tumor microenvironment remodeling, providing a promising and novel therapeutic strategy for LUAD.

Improved Predictive Model of Drivers’ Subjective Perception of Vehicle Reaction under Aerodynamic Excitations

Improved Predictive Model of Drivers’ Subjective Perception of Vehicle Reaction under Aerodynamic Excitations

Human Inspired Autonomous Intersection Handling Using Game Theory

Left turning for autonomous vehicles at intersections is challenging due to the various driving behaviors from different human drivers and the strong interaction between the autonomous vehicle and human traffic participants. This paper proposes a planning and decision making framework for intersection left-turning which considers the interaction between autonomous vehicles and human drivers as well as pedestrians to address this issue. The proposed framework considers interactions mathematically by formulating the problem as a linear quadratic differential game. Through solving the Nash equilibrium of the game, the autonomous vehicle is able to properly interact with surrounding traffic participants. Under the differential game framework, the accuracy of the interaction formulation is closely related to the behavior model of human drivers. Therefore, real-world human behavior is extracted and evaluated from naturalistic driving dataset to help establish more realistic modeling and estimation of various kinds of traffic participants, including aggressive, neutral and conservative traffic participants. The simulation results show that the autonomous vehicle is able to properly estimate the types of traffic participants by observing their behavior using the proposed technique. Then the autonomous vehicle behave according to the types of those traffic participants to enable interactive and human-like planning and decision making at intersections.

Determinants of Vaccine Hesitancy among Home Health Care Service Recipients in Saudi Arabia.

Vaccine hesitancy has been identified by the World Health Organization (WHO) as a major worldwide health threat. Home Health Care (HHC) service recipients represent a vulnerable group and were prioritized to receive coronavirus disease (COVID-19) vaccination during the national vaccine campaigns in Saudi Arabia. We aimed to investigate the most frequent reasons for vaccine hesitancy among home health care recipients in Saudi Arabia. This cross-sectional survey was conducted among home health care (HHC) service recipients in Saudi Arabia from February 2022 to September 2022. The behavioral and social drivers (BeSD) model developed by the WHO was used to understand the factors affecting vaccination decision making in our cohort. Of the 426 HHC service recipients enrolled in the study, a third were hesitant to complete the COVID-19 vaccination series. The most prevalent reported reason for COVID-19 vaccine refusal was concerns about the vaccine side effects (41.6%). Factors independently associated with COVID-19 vaccination hesitancy were: having chronic conditions (odds ratio [OR] = 2.59; 95% confidence interval [CI] = 1.33-5.05, p = 0.005), previous COVID-19 diagnosis (OR = 0.48; 95% CI: 0.28-0.82, p = 0.008), ease of getting the COVID-19 vaccine by themselves (OR = 0.49; 95% CI: 0.28-0.89, p = 0.018), belief in the importance of COVID-19 vaccine in protecting their health (OR = 0.60; 95% CI: 0.38-0.96, p = 0.032), and confidence in the safety of COVID-19 vaccination (OR = 0.38; 95% CI: 0.21-0.69, p = 0.001). Only one-third of the study participants were hesitant to complete the series of COVID-19 vaccination. Understanding the factors underpinning vaccine hesitancy among this group would help healthcare workers and policymakers in developing personalized health awareness campaigns aimed at improving vaccine acceptance levels.

Beyond adaptive cruise control and lane centering control: drivers' mental model of and trust in emerging ADAS technologies.

The potential safety benefits of advanced driver assistance systems (ADAS) highly rely on drivers' appropriate mental models of and trust in ADAS. Current research mainly focused on drivers' mental model of adaptive cruise control (ACC) and lane centering control (LCC), but rarely investigated drivers' understanding of emerging driving automation functions beyond ACC and LCC. To address this research gap, 287 valid responses from ADAS users in the Chinese market, were collected in a survey study targeted toward state-of-the-art ADAS (e.g., autopilot in Tesla). Through cluster analysis, drivers were clustered into four groups based on their knowledge of traditional ACC and LCC functions, knowledge of functions beyond ACC and LCC, and knowledge of ADAS limitations. Predictors of driver grouping were analyzed, and we further modeled drivers' trust in ADAS. Drivers in general had weak knowledge of LCC functions and functions beyond ACC and LCC, and only 27 (9%) of respondents had a relatively strong mental model of ACC and LCC. At the same time, years of licensure, weekly driving distance, ADAS familiarity, driving style (i.e., planning), and personability (i.e., agreeableness) were associated with drivers' mental model of ADAS. Further, it was found that the mental model of ADAS, vehicle brand, and drivers' age, ADAS experience, driving style (i.e., focus), and personality (i.e., emotional stability) were significant predictors of drivers' trust in ADAS. These findings provide valuable insights for the design of driver education and training programs to improve driving safety with ADAS.

Prediction of the distribution of tractor driver’s eye positions

A predictive model of tractor driver’s eye positions based on the anthropometric parameters and tractor cab layout parameters was established using the statistical approach. To our knowledge, this model is the first published predictive model of tractor drivers’ eye positions that takes account of their body dimensions and tractor types. In order to predict the distribution of the drivers’ eye positions precisely, the fore-and-aft positions of the driving seat determined by 180 tractor drivers in three different tractors and the relative positions of the eyes to the designated reference points were recorded to quantitatively demonstrate the distribution rule of their eye positions. The multiple linear regression and principal component analysis were adopted to processing the feature parameters of human and tractor, and the prediction effect of the model was assessed according to the proportion of the driver’s eye positions from side view those were within the range of the eyellipse model. The experimental result revealed that the centroid positions and principal axis dip angle of the model differed from each other respectively, while the axis lengths were the same. The principal axis dip angle was horizontal or oblique forward-and-upward. And the tractor layout parameters had a significant impact on the centroid coordinate and principal axis dip angle of the model, while the dimension of the model was associated with the anthropometric parameters mainly. The prediction accuracy of the model exceeded 80%. The regression predictive model provides a reference tool for the visual field design of tractor displays and cab layouts.

Drivers of customers' efforts to cope with wearable device usage problems and the role of demographics in a study in China

PurposeThis study aims to investigate determinants such as performance expectancy (PE), health beliefs (HB), technological compatibility, trust and social influence (SI) of customers' efforts to cope with problems associated with using wearable health technology, thereby determining their use intention of such products. The moderating effect of the demographic variables is also investigated.Design/methodology/approachAn online survey was used to collect panel data from 285 respondents in China. Structural equation modeling was used to test the hypotheses, and interaction moderation was applied to test the moderating effect of the demographic variables.FindingsThe results show that HB, technological compatibility, trust and SI significantly impact coping efforts associated with usage, which significantly influences intention to use. Only income significantly moderated the effect of coping efforts on the intention to use.Originality/valueThis is the first study combining coping theory and UTAUT to develop a model of drivers that encourages customers to overcome the constraints of using wearable technologies.

The effectiveness of Driver’s model in teaching mathematics for developing intermediate school students conceptual understanding

This study aims to investigate the effectiveness of Driver’s model (DM) in developing the conceptual understanding (CU) of first-class intermediate students in geometry and polygons via quasi-experimental design, for a random sample of 62 female-students divided into two groups, the experimental group with 31 students who were taught the module by DM, and the control group with 31 students were taught the module by the traditional teaching method. The study found significant statistical variances regarding scores means between the two groups at the three levels of explanation, interpretation, and application for the experimental group with a high impact reached at0.31 along with a high impact, too, regarding the mean of the total score of CU test for the same group, which reached at 0.43. Thus, the study recommends using DM in mathematics teaching.

Work Life Balance Model, Work Stress and Performance of Millennial Generation Online Taxibike Drivers

The characteristics of the millennial generation are different from those of the previous generation. It is important to research the performance of those who currently dominate the world of work, especially the unprecedented work of online motorcycle taxi drivers. The purpose of this study is to determine the balance and work stress on the performance of millennial generation online motorcycle taxi drivers. This study used a quantitative approach, with the subject of online motorcycle taxi drivers, where the population is difficult to know. The sample was determined using the Lameshow formula so that a sample of 100 respondents was obtained. Teknik purposive sampling is used with the criteria of married drivers and classified as millennial generation. Data collection using questionnaires in the form of google forms is distributed directly to online motorcycle taxi drivers. Data analysis techniques use multiple linear regression analysis. The results showed that there was an effect of work life balance on performance, but on the contrary, there was no effect of work stress on performance.

Towards a better understanding of volunteer engagement: self-determined motivations, self-expression needs and co-creation outcomes

PurposeNon-profit organizations (NPO) contribute significantly to the welfare of citizens and communities. Engagement in volunteering is crucial for sustaining volunteer motivation and for the effective and efficient functioning of NPO, with significant implications for society at large. Yet, literature on volunteer engagement (VE) is limited to date. Grounded on service-dominant logic, self-congruity theory and self-determination theory, this study aims to understand what motivates VE and how it may evolve into a co-creation process valuable to NPO and its stakeholders.Design/methodology/approachBased on survey data collected from 450 volunteers, working with a diverse set of NPO, a comprehensive model of drivers and outcomes of VE was empirically tested using PLS-SEM, considering the mediating role of volunteers' congruence with the core values of the NPO.FindingsThe impact of volunteers' perceived autonomy, competence and relatedness on VE and its subsequent role in volunteers' loyalty and extra-role engagement behaviors (i.e. co-development, influencing and mobilizing behaviors) were validated. Moreover, the study validates value congruence as an internalizing mediating mechanism in the engagement process, a role that has been implied but not empirically tested.Originality/valueThe study contributes to the engagement and volunteering literature, which despite an unprecedented parallel have developed almost independently, with limited reference to one another. As the nomological network of VE is still underexplored, the study extends the engagement literature to the volunteering sector, validating the key (but underexplored) role of self-determination needs and value congruence in driving VE and value co-creation behaviors. The study further adds to engagement research while addressing other actors' engagement beyond the customer–brand dyad. While adopting a seldom explored marketing perspective of VE, this study provides NPO valuable insights on how to manage and engage volunteers.

“The new normal has become a nonstop crisis”: a qualitative study of burnout among Philadelphia’s harm reduction and substance use disorder treatment workers during the COVID-19 pandemic

BackgroundThe COVID-19 pandemic worsened the ongoing overdose crisis in the United States (US) and caused significant mental health strain and burnout among health care workers (HCW). Harm reduction, overdose prevention, and substance use disorder (SUD) workers may be especially impacted due to underfunding, resources shortages, and chaotic working environments. Existing research on HCW burnout primarily focuses on licensed HCWs in traditional environments and fails to account for the unique experiences of harm reduction workers, community organizers, and SUD treatment clinicians.MethodsWe conducted a qualitative secondary analysis descriptive study of 30 Philadelphia-based harm reduction workers, community organizers, and SUD treatment clinicians about their experiences working in their roles during the COVID-19 pandemic in July–August 2020. Our analysis was guided by Shanafelt and Noseworthy’s model of key drivers of burnout and engagement. We aimed to assess the applicability of this model to the experiences of SUD and harm reduction workers in non-traditional settings.ResultsWe deductively coded our data in alignment with Shanafelt and Noseworthy’s key drivers of burnout and engagement: (1) workload and job demands, (2) meaning in work, (3) control and flexibility, (4) work-life integration, (5) organizational culture and values, (6) efficiency and resources and (7) social support and community at work. While Shanafelt and Noseworthy’s model broadly encompassed the experiences of our participants, it did not fully account for their concerns about safety at work, lack of control over the work environment, and experiences of task-shifting.ConclusionsBurnout among healthcare providers is receiving increasing attention nationally. Much of this coverage and the existing research have focused on workers in traditional healthcare spaces and often do not consider the experiences of community-based SUD treatment, overdose prevention, and harm reduction providers. Our findings indicate a gap in existing frameworks for burnout and a need for models that encompass the full range of the harm reduction, overdose prevention, and SUD treatment workforce. As the US overdose crisis continues, it is vital that we address and mitigate experiences of burnout among harm reduction workers, community organizers, and SUD treatment clinicians to protect their wellbeing and to ensure the sustainability of their invaluable work.

How psychological factors affect speeding behavior: Analysis based on an extended theory of planned behavior in a Chinese sample

Speeding, the most frequent traffic offense, is a key contributor to road crashes. This study explores the psychological factors affecting drivers’ speeding behavior (exceeding the posted speed limit by more than 10%) based on the extended theory of planned behavior (TPB). The scale incorporating TPB constructs and additional variables (Type A personality, risk perception, and driving habit) was developed to collect empirical data, and 945 valid samples were obtained. The reliability and validity of the developed scales were verified, and a structural equation model of drivers’ speeding behavior was established to explore the causal relationship between speeding behavior and psychological characteristics and the interrelationships between TPB variables (speeding intention, attitude, subjective norm, and perceived behavioral control), explicit cognition predictors (risk perception and Type A personality) and implicit habitual factors (driving habit). The results showed that speeding intention and driving habit positively correlated with self-reported speeding behavior. In addition, attitude and risk perception positively correlated with speeding intention. Although Type A personality trait had no significant effects on speeding behaviors, it had significant effects on attitude and risk perception. In addition, the results of the mediation analysis demonstrated that speeding intention, driving habit, and attitude toward speeding are the most influential factors. Attitude and risk perception have a significant indirect effect on speeding behavior mediated by speeding intention. Drivers’ attitudes, driving habits, and risk perception played a mediating role in the relationships between Type A personality traits and speeding behavior. The findings yield insights into useful information and directions for designing road safety interventions, developing driver education and training programs, and strengthening social messaging aimed at the general public.