Analyzing Drivers’ Mental Patterns Using Q-Methodology

The human factor is the most important cause of road accidents. Investigating the drivers’ mental patterns can lead to a better understanding of the factors that affect drivers to make a mistake and thus increase the likelihood of an accident. In this study, mental patterns of drivers as a human characteristic are determined through a questionnaire survey. To do this, 166 participants (18 - 65 years) were asked to express their opinion on the possible effect of 25 factors on the occurrence of accidents. These factors were selected through the investigation of the accident database during the last three years in different areas of the case study. To analyze the data extracted from the survey, Q-methodology was used. The results of the factor analysis showed that there are 5 mental patterns among the participants. Based on the driver’s opinion, human factors and road conditions were the most and least influential accident-generating items, respectively. The most significant reason for accidents determined by drivers was human errors including 1) unauthorized overtaking, 2) unauthorized speed, 3) driver distractions (such as cell phone), and 4) driver physical disability (such as visual impairment). Moreover, the failure of the vehicle was mostly reported as another influential contributor to accidents. It is worth mentioning that the results of this study can be used to minimize accidents resulted from drivers’ behavioral errors by suggesting strategies for enhancing their performance through new manuals which is a step towards a safer road.

A Comparison between Major Factor Extraction and Factor Rotation Techniques in Q-Methodology

The statistical analysis in Q-methodology is based on factor analysis followed by a factor rotation. Currently, the most common factor extraction methods are centroid and principal component extractions and the common techniques for factor rotation are manual rotation and varimax rotation. However, there are some other factor extraction methods such as principal axis factoring and factor rotation methods such as quartimax and equamax which are not used by Q-users because they have not been implemented in any major Q-program. In this article we briefly explain some major factor extraction and factor rotation techniques and compare these techniques using three datasets. We applied principal component and principal axis factoring methods for factor extraction and varimax, equamax, and quartimax factor rotation techniques to three actual datasets. We compared these techniques based on the number of Q-sorts loaded on each factor, number of distinguishing statements on each factor, and excluded Q-sorts. There was not much difference between principal component and principal axis factoring factor extractions. The main findings of this article include emergence of a general factor and a smaller number of excluded Q-sorts based on quartimax rotation. Another interesting finding was that a smaller number of distinguishing statements for factors based on quartimax rotation compared to varimax and equamax rotations. These findings are not conclusive and further analysis on more datasets is needed.

Using Cohen’s Effect Size to Identify Distinguishing Statements in Q-Methodology

Q-methodology was introduced more than 80 years ago to study subjective topics such as attitudes, perceptions, preferences, and feelings and there has not been much change in its statistical components since then. In Q-methodology, subjective topics are studied using a combination of qualitative and quantitative techniques. It involves development of a sample of statements and rank-ordering these statements by study participants using a grid known as Q-sort table. After completion of Q-sort tables by the participants, a by-person factor analysis (i.e., the factor analysis is performed on persons, not variables or traits) is used to analyze the data. Therefore, each factor represents a group of individuals with similar views, feelings, or preferences about the topic of the study. Then, each group (factor) is usually described by a set of statements, called distinguishing statements, or statements with high or low factor scores. In this article, we review one important statistical issue, i.e. the criteria for identifying distinguishing statements and provide a review of its mathematical calculation and statistical background. We show that the current approach for identifying distinguishing statements has no sound basis, which may result in erroneous findings and seems to be appropriate only when there are repeated evaluations of Q-sample from the same subjects. However, most Q-studies include independent subjects with no repeated evaluation. Finally, a new approach is suggested for identifying distinguishing statements based on Cohen’s effect size. We demonstrate the application of this new formula by applying the current and the suggested methods on a Q-dataset and explain the differences.

Enabling affordances of blockchain in agri-food supply chains:A value-driver framework using Q-methodology

The application of blockchain beyond cryptocurrencies has received increasing attention from industry and scholars alike.Given predicted looming food crises,some of the most impactful deployments of blockchains are likely to concern food supply chains.This study outlined how blockchain adoption can result in positive affordances in the food supply chain.Using Q-methodology,this study explored the current status of the agri-food supply chain and how blockchain technology could be useful in addressing existing challenges.This theorization leads to the proposition of the 3TIC value-driver framework for determining the enabling affordances of blockchain that would increase shared value for stakeholders.First,we propose a framework based on the most promising features of blockchain technology to overcome current challenges in the agri-food industry.Our value-driver framework is driven by the Q-study findings of respondents closely associated with the agri-food supply chain.This framework can provide supply chain stakeholders with a clear perception of blockchain affordances and serve as a guideline for utilizing appropriate features of technology that match organizations’capabilities,core competencies,goals,and limitations.Therefore,it could assist top-level decision-makers in systematically evaluating parts of the organization to focus on and improve the infrastructure for successful blockchain implementation along the agri-food supply chain.We conclude by noting certain significant challenges that must be carefully addressed to successfully adopt blockchain technology.

Exploring perspectives in assessing the quality of governance of the Reducing Emissions from Deforestation and Forest Degradation (REDD+) pilot project in Cambodia: Use of Q Methodology

Public and policy makers alike are concerned about national and global deforestation and forest degradation. These issues pose a significant threat to social, economic and environmental welfare.Attempts to prevent forest loss and increased attention to pilot REDD+ projects in community forestry sites would both deliver rural livelihood benefits and help to reduce adverse climate impacts.However, there has been no significant exploration of the viewpoints of local experts to determine the monitoring and action needed to support communitybased forestry and improve the governance of REDD+pilot projects in Cambodia. Therefore, this study aimed to assess the perceptions of local stakeholders towards the quality of governance of the first community forest REDD+ pilot project in Cambodia,employing Q-methodology. We adapted 11 indicators of the hierarchical framework of assessment of governance quality to design 40 Q-statements related to REDD+ governance or achievements. The 52 P-set ranked these Q-statements with respect to the community-based REDD+ pilot project. Our study revealed that local stakeholders held four distinct, and partially opposite, views, that:(1) the REDD+ project is successful because it is inclusiveness and capable of causing behavioral change;(2) REDD+ pilot projects should be led by government, not external or locally;and needs more resources;(3) the REDD+ pilot project has raised unrealistic expectations, would likely be a source of corruption and will probably not be successful for local people or halting deforestation;and(4) the REDD+ pilot project is inclusive but not very transparent and probably ineffective at protecting forest. Through these four varied perspectives from local people involved in the project,we can see that there remain serious challenges to the future of pilot community forestry REDD+ projects,including the complex interaction between the multinational actors and the local socio-ecological systems.To move forwards, this study suggested Cambodia should make a pro-poor REDD+ program,implementing more community-based REDD+projects which explicitly build the assets and capacity of the poorest households. This study also shows that Q-methodology can highlight the diverse viewpoints of local stakeholders concerning the quality of community forest REDD+ governance, helping policy makers, implementers and local stakeholders to better identify the challenges to be addressed.