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Purpose – Urban and suburban arterials carry a large share of urban traffic and contend with a relatively large proportion of transport network crashes. Road crashes and their consequent societal costs diminish the sustainability of transportation systems, highlighting the need to identify road safety problems and their corresponding solutions. This chapter briefly outlines problems and solutions associated with crash risk on urban and suburban arterials. In addition, this chapter studies and discusses several safety countermeasures – ranging from local treatments to integral frameworks – and their effectiveness on improving traffic safety of urban and suburban arterials.

Approach – Crash occurrence on urban and suburban arterials is affected by numerous contributing factors. This chapter pays attention primarily to the effects of traffic characteristics and road design features. In this regard, several pertinent variables which have been extensively examined in the literature are reviewed and their contributions to the safety of urban and suburban arterials are discussed.

Findings – A review of the literature identifies a number of variables as influential factors of crashes on urban and suburban arterials. Although the associations of some variables (e.g., traffic volume) are consistent with expectations, others (e.g., lane width and speed) show mixed and sometimes counterintuitive results. These findings signify that additional research is needed to reveal the correct functional form and magnitude of these relationships.

Practical implications – The results show that while the general direction and magnitude of effects of some engineering and management-related treatments are known, additional research is needed to consolidate the impact and effectiveness of integrated approaches.

This study aims to explore and empirically test variables influencing material delivery schedule inaccuracies?

A mixed-method case approach is applied. Explanatory variables are identified from the literature and explored in a qualitative analysis at an automotive original equipment manufacturer. Using logistic regression and random forest classification models, quantitative data (historical schedule transactions and internal data) enables the testing of the predictive difference of variables under various planning horizons and inaccuracy levels.

The effects on delivery schedule inaccuracies are contingent on a decoupling point, and a variable may have a combined amplifying (complexity generating) and stabilizing (complexity absorbing) moderating effect. Product complexity variables are significant regardless of the time horizon, and the item’s order life cycle is a significant variable with predictive differences that vary. Decoupling management is identified as a mechanism for generating complexity absorption capabilities contributing to delivery schedule accuracy.

The findings provide guidelines for exploring and finding patterns in specific variables to improve material delivery schedule inaccuracies and input into predictive forecasting models.

The findings contribute to explaining material delivery schedule variations, identifying potential root causes and moderators, empirically testing and validating effects and conceptualizing features that cause and moderate inaccuracies in relation to decoupling management and complexity theory literature?

This study investigated the joint impact of organizational and individual career management on employees' ideal self-discrepancy. Drawing on the identity literature, the authors aimed to uncover the mechanism and boundary condition of this impact, focusing on how organizations influence ideal and actual selves of employees with different protean career orientation.

The authors conducted a four-wave time-lagged study over eight months, with a sample of 331 employees from various organizations.

Perceived organizational career management negatively influenced ideal self-discrepancy via organizational identification, and such relationship was strengthened by protean career orientation. Employees with stronger protean career orientation saw a stronger moderating effect of individual career management on the relationship between organizational identification and ideal self-discrepancy, whereas their counterparts saw an opposite effect.

This study highlighted the essential role of organization in narrowing employees' ideal self-discrepancy in the protean career era. It suggested that organizations should set differentiated career practices depending on employees' protean career orientation levels.

By integrating vocational psychology and organizational scholarship, this study extended the ideal self-discrepancy literature by offering a nuanced understanding of the mechanism and boundary condition of the role of organizational career management in narrowing ideal self-discrepancy in the protean career era. It identified the joint efforts of organization and employee as a fascinating avenue for future studies.

Although numerous studies have been conducted to explore the impact of various factors on employees' turnover intention and intention to remain with the organization, the relationship between these two constructs remains largely unexplored. Considering the significance of these constructs, particularly in the context of the COVID-19 pandemic, the authors aimed to investigate their association within an academic environment using a dynamic modeling approach.

This study follows a quantitative approach and utilizes a longitudinal survey design. The authors utilized a cross-lagged panel model (CLPM) and employed the parametric efficient partial least squares (PLSe2) methodology to estimate the dynamic model using data gathered from lecturers associated with both public and private universities in Malaysia. In order to offer methodological insights to applied higher education researchers, the authors also compared the results with maximum likelihood (ML) estimation.

The findings of the authors' study indicate a reciprocal relationship between turnover intention and intention to remain with the organization, with intention to remain with the organization being a stronger predictor. Moreover, situational factors were found to have a greater influence on eliciting turnover intention within academic settings. As anticipated, the use of the PLSe2 methodology resulted in higher R² values compared to ML estimation, thereby reinforcing the effectiveness of PLS-based methods in explanatory-predictive modeling in applied studies.

The authors' findings suggest prioritizing policies that enhance training and consultation sessions to foster positive attitudes among lecturers. Positive attitudes significantly impact judgment-driven behaviors like turnover intention and intention to remain with the organization. Additionally, improving working environments, which indirectly influence judgment-driven behaviors through factors like affective work events, affect and attitudes, should also be considered.

This study pioneers the examination of the causal relationship between turnover intention and intention to remain with the organization, their stability over time and the association of changes in these variables using a dynamic CLPM in higher education. It introduces the novel application of the cutting-edge PLSe2 methodology in estimating a CLPM, providing valuable insights for researchers in explanatory-predictive modeling.

The environment in which users acquire medical and health information has changed dramatically, with online health communities (OHCs) emerging as an essential means for accessing health information. It is imperative to comprehend the factors that shape the users' compliance willingness (UCW) to health information in OHCs.

This study adopted the information adoption model (IAM) and theory of planned behavior (TPB) to investigate the influence of argument quality (AQ), source credibility (SC) and subjective norms (SN) on UCW while considering the two types of online health information – mature and emerging treatments. The authors conducted an explanatory-predictive study based on a 2 (treatment types: mature vs. emerging) * 2 (AQ: high vs. low) * 2 (SC: high vs. low) scenario-based experiment, using the partial least squares structural equation modeling (PLS-SEM).

SC positively influences AQ. AQ, SC and SN contribute to information usefulness (IU). These factors positively affect UCW through the mediation of IU. SN were found to improve UCW directly. Moreover, the moderating effect of SC on AQ and IU was more substantial for emerging treatments.

The research model integrates IAM and TPB, considering information types as an additional variable. The approach and findings provide a valuable explanation for UCW to health information in OHCs.

It is increasingly accepted that the chief purchasing officer (CPO), as the highest ranking member of the purchasing and supply management (PSM) function, is a resource that can add strategic value to the firm. Delineating the organizational and human capital factors that determine CPO compensation packages can help firms maintain their competitive advantage by attracting and retaining talent in this position. Although an extensive literature base examines executive compensation, such research at levels below the CEO is sparse. Based on the rich literature discussing executive compensation as well as a survey of Fortune 500 CPOs, examines the influence of organizational and human capital on CPO compensation from a resource‐based view of the firm. The organizational capital characteristics of annual sales, purchases as a percent of sales, and the number of reporting levels between the CPO and CEO were found to influence CPO compensation significantly. Somewhat surprisingly, CPO age was the only human capital factor of those tested (years in PSM, education level, CPM certifications) that significantly influenced compensation.

Even though social media (SM) has been explored in-depth, its role remains unclear regarding short- and long-term preventive attitudes in global health emergencies. To fill this gap, the Stimulus-Organism-Response framework aims to clarify the social media exposure mission in acknowledging risk perception and triggering preventive attitudes and behaviors toward COVID-19 and general vaccination.

The authors conducted an explanatory-predictive study on 480 Romanian students, using partial least squares structural equation modeling, and performed model evaluation, multi-group, model selection, and importance-performance map analyses.

The study provides insights in understanding significant relationships and drivers explaining and predicting attitudes towards vaccines. The main relationships are between fear and risk perception; risk and preventive attitudes and behaviors; and vaccination degree and attitudes to vaccines. The most important factor is the vaccination degree and media exposure is the most performant.

Developing and applying regulations and communication strategies for quality mass information may positively increase attitudes toward vaccines by indirectly enforcing the main drivers.

Organizations, authorities, and opinion leaders must have a coherent supportive presence in media.

This study filled the literature gap by building a generic theoretical and empirical proven framework that investigates the mediated effect towards vaccines of all media types by COVID-19 experience and vaccination degree.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-11-2021-0621

Attempts to describe the determinants of rent. Describes the initial stages in the development of a regional office rent prediction model which uses readily available data and should aid the investment decision‐making process. Rejects cross‐sectional analysis, preferring time series approaches. Formulates a spatially disaggregated model which allows for delays between changes in user output and changes in user demand, and which reflects the variable adjustment rate between these two factors. Argues that the combined influence of the independent variables in the derived equation can explain up to 97 per cent of the variation in rent over the period examined.

The aim of this paper is to introduce and evaluate an expanded model approximating variety‐seeking behaviour (VSB), which is defined as the phenomenon that consumers switch between brands because of the utility gained from the switch itself.

As a theoretical basis, two existing approaches, the switch (S) and the successive switch (SS) models, were examined and adjusted. The integration of both approaches led to the expanded “switch of brands” (SB) model. SB was tested empirically using panel data for a highly developed food market (the Austrian food market for three product categories, i.e. fruit juice, fruit yoghurt, and chocolate, with data for 2007 and 2008).

The integration of S and SS into the expanded SB model succeeded. Based on an annual comparison, the reliability of the SB model was tested. Since the results can be reproduced, it can be determined that the SB model is reliable. To test the explanatory power, a variable assessing brand loyalty (“brand purchase frequency”) was introduced. Correlation analyses showed that the expanded variety‐seeking model is more appropriate for approximating VSB than either S or SS.

The SB model is still very basic, integrating only three variables. Other influencing variables have to be considered to further improve the significance of the SB model.

The expanded VSB model can be applied in all product categories and markets where panel data are available. It helps to analyse panel data in view of consumers' variety seeking and brand switching behavior.

The expanded variety‐seeking model (SB) is easy to implement and gives valid information on VSB. It can be used for marketing decisions, especially in connection to brand management.