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Within the last 15 years, law enforcement agencies have increased their collection of data on vehicle stops. A variety of resource guides, research reports, and peer‐reviewed articles have outlined the methods used to collect these data and conduct analyses. This literature is spread across numerous publications and can be cumbersome to summarize for practical use by practitioners and academics. This article seeks to fill this gap by detailing the current best practices in vehicle stop data collection and analysis in state police agencies.

The article summarizes the data collection techniques used to assist in identifying racial/ethnic disparities in vehicle stops. Specifically, questions concerning why, when, how, and what data should be collected are addressed. The most common data analysis techniques for vehicle stops are offered, including an evaluation of common benchmarking techniques and their ability to measure at‐risk drivers. Vehicle stop outcome analyses are also discussed, including multivariate analyses and the outcome test. Within this summary, strengths and weaknesses of these techniques are explored.

In summarizing these approaches, a body of best practices in vehicle stop data collection and analysis is developed.

Racial profiling continues to be a contentious issue for law enforcement and the community. A considerable body of research has developed to assess the prevalence of racial profiling. This article offers social scientists and practitioners a comprehensive, succinct, peer‐reviewed summary of the best practices in vehicle stop data collection and analysis.

The purpose of this paper is to explain how rapid ethnography (RE) is used to understand the business decision-making process of micro-entrepreneurs. The objective of this paper is to highlight the applicability of RE in entrepreneurship research and outline practical strategies that can be used by future RE researchers.

This paper is written as a reflection using the author’s experience in using RE.

This paper highlights that RE can be used as a research technique in entrepreneurship research. The study shows how to incorporate technological advances into RE without violating the underlying ethnographic principles. The paper also explains how preparation, planning, technology-assisted techniques, non-traditional socialisation processes, and multiple and parallel data collection strategies enhance the effectiveness of RE. The paper outlines practical strategies for researchers such as collaborations, using field guides, clear schedules and time gaps in the data collection.

Although RE is widely used in research related to human-computer interactions, medicine, education and marketing, RE in entrepreneurship research seems to be limited. Thus, this paper explores this gap and contributes to the scholarly field of entrepreneurship research by highlighting the methodological potential of RE. In addition, the paper contributes empirically to the qualitative research domain by explaining practical steps in using RE.

Background: The annual average daily traffic (AADT) data from road segments are critical for roadway projects, especially with the decision-making processes about operations, travel demand, safety-performance evaluation, and maintenance. Regular updates help to determine traffic patterns for decision-making. Unfortunately, the luxury of having permanent recorders on all road segments, especially low-volume roads, is virtually impossible. Consequently, insufficient AADT information is acquired for planning and new developments. A growing number of statistical, mathematical, and machine-learning algorithms have helped estimate AADT data values accurately, to some extent, at both sampled and unsampled locations on low-volume roadways. In some cases, roads with no representative AADT data are resolved with information from roadways with similar traffic patterns.

Methods: This study adopted an integrative approach with a combined systematic literature review (SLR) and meta-analysis (MA) to identify and to evaluate the performance, the sources of error, and possible advantages and disadvantages of the techniques utilized most for estimating AADT data. As a result, an SLR of various peer-reviewed articles and reports was completed to answer four research questions.

Results: The study showed that the most frequent techniques utilized to estimate AADT data on low-volume roadways were regression, artificial neural-network techniques, travel-demand models, the traditional factor approach, and spatial interpolation techniques. These AADT data-estimating methods' performance was subjected to meta-analysis. Three studies were completed: R squared, root means square error, and mean absolute percentage error. The meta-analysis results indicated a mixed summary effect: 1. all studies were equal; 2. all studies were not comparable. However, the integrated qualitative and quantitative approach indicated that spatial-interpolation (Kriging) methods outperformed the others.

Conclusions: Spatial-interpolation methods may be selected over others to generate accurate AADT data by practitioners at all levels for decision making. Besides, the resulting cross-validation statistics give statistics like the other methods' performance measures.

The widespread acceptance of the use of online techniques in market research necessitates appreciation of the relative advantages and disadvantages of these techniques over more traditional research methods. This paper reports on a study which directly compares online and postal data collection methods using the same survey instrument on two samples drawn from the same population of football club subscribers. The results confirm that the online and postal respondents are demographically different. Online data collection is shown to be less expensive per respondent and that data collection is faster, however, an overall lower response level is achieved relative to the postal data collection method. Of greater importance, though, are the findings that respondents seem to answer questions differently online than they do via postal methods. The conclusion here is that online data collection should not be treated as a direct substitute for postal data collection in every instance.

In order to predict the future we must examine the past in order to observe trends over periods of time and establish the degree of probability with which these trends are likely to repeat themselves in the future. All forecasts are wrong, and management must be aware of this fact and decide upon the degree of inexactitude that can be tolerated when planning for the future.

In this paper, we present a proposed information environment (PROPIE) for enhanced interaction and value‐adding of electronic documents (e‐documents). The design of PROPIE was based on a thorough user needs and requirements assessment in interacting with information through well‐documented findings, and a focus group with twelve participants to elicit features that were deemed desirable in future interactions. The design was also based on an earlier work which reviewed the advancements in various user interface (UI) technologies, visualisation and interactive techniques, and a consideration of novel information structuring and organisation techniques that pose important implications for the design of more advanced UIs. Providing a suite of novel features and interactive tools that can be flexibly combined, PROPIE allows users to apply multiple novel ways to query intuitively and navigate information in an e‐document. The querying and browsing processes in PROPIE are supported by various interactive and visualisation techniques. Users work within a visually sovereign, integrated environment for information gathering and organising, based on navigable, fractional information objects that are also affiliated with rich metadata and additional layers of value‐adding information. A set of interface mock‐ups was developed to demonstrate the potential of the environment in supporting the design of a new generation of electronic journals (e‐journals). We report here empirical results from a study conducted to obtain representative users‘ feedback with regard to using PROPIE for interacting with e‐journals. Twenty‐two participants from a variety of academic backgrounds participated in the evaluation. Overall, PROPIE was found to have the potential both for enhancing the user’s interaction with information captured within e‐journals and for adding value to e‐documents in various ways.

This paper is an interim report of a study under way in the USA with the goal of developing a core set of national statistics and performance measures that librarians,researchers, and policy‐makers can use to describe public library and library‐based state‐wide network use of the Internet and Web‐based services and resources. The paper summarises preliminary findings and key issues identified as of January 2000. It describes a number of models for developing such statistics and performance measures. The paper also offers a number of preliminary statistics and performance measures that are being field‐tested to describe information resources and services in the networked environment. The authors expect to have a final set of such statistics and performance measures by the summer of 2000.

This article aims to explore valuable insights into the construction progress monitoring (CPM) research domain, which include main research topics, knowledge gaps and future research themes. For a long time, CPM has been significantly researched with increasing enthusiasm. Although a few review studies have been carried out, there is non-existence of a quantitative review study that can deliver a holistic picture of CPM.

The science mapping-based scientometric analysis was systematically processed with 1,835 CPM-related journal articles retrieved from Scopus. The co-authorship analysis and direct citation analysis were carried out to identify the most influential researchers, countries and publishers of the knowledge domain. The co-occurrence analysis of keyword was assessed to reveal the most dominating research topics and research trend with the visual representation of the considered research domain.

This study reveals seven clusters of main research topics from the keyword co-occurrence analysis. The evolution of research confirms that CPM-related research studies were mainly focused on fundamental and traditional CPM research topics before 2007. The period between 2007 and 2020 has seen a shift of research efforts towards digitalization and automation. The result suggests Building Information Modelling (BIM) as the most common, growing and influential research topic in the CPM research domain. It has been used in combination with different data acquisition technologies (e.g. photogrammetry, videogrammetry, laser scanning, Internet of Things (IoT) sensors) and data analytics approaches (e.g. machine learning and computer vision).

This study provides the horizon of potential research in the research domain of CPM to researchers, policymakers and practitioners by availing of main research themes, current knowledge gaps and future research directions.

This paper represents the first scientometric study depicting the state-of-the-art of the research by assessing the current knowledge domain of CPM.

Qualitative research that involves the use of human participants calls for the need to protect those participants to give their honest view during data collection. This is an important part of every primary data collection in qualitative studies using interviews. This paper aims to investigate all available ethical considerations that need to be observed by the researcher when conducting primary data collection through interview and to explore the theories that underpin the ethics in qualitative studies.

This paper systemically reviewed existing qualitative data on ethics and gathered information that were analysed and presented on the topic area.

The findings show that ethical considerations deal with the various approaches adopted by the researcher to make the participants feel safe to participate in any given researcher. During an interview process in qualitative research, the findings show that anonymity, voluntary participation, privacy, confidentiality, option to opt out and avoiding misuse of findings are ethical considerations that must be observed by the researcher. The outcome of the investigation also shows that deontology and utilitarianism, rights and virtue are the main theories that underpin ethical considerations in research.

The rights of the research participants need to be respected in qualitative research to assist in gathering accurate information to achieve the objectives of study. This and other ethical principles such as anonymity, privacy, confidentiality, voluntary participation and option to opt out guide the researcher to systematically adhere to data collection approaches that yield valid results in qualitative data collection using interviews.

Prediction is a critical task in targeted online advertising, where predictions better than random guessing can translate to real economic return. This study aims to use machine learning (ML) methods to identify individuals who respond well to certain linguistic styles/persuasion techniques based on Aristotle’s means of persuasion, rhetorical devices, cognitive theories and Cialdini’s principles, given their psychometric profile.

A total of 1,022 individuals took part in the survey; participants were asked to fill out the ten item personality measure questionnaire to capture personality traits and the dysfunctional attitude scale (DAS) to measure dysfunctional beliefs and cognitive vulnerabilities. ML classification models using participant profiling information as input were developed to predict the extent to which an individual was influenced by statements that contained different linguistic styles/persuasion techniques. Several ML algorithms were used including support vector machine, LightGBM and Auto-Sklearn to predict the effect of each technique given each individual’s profile (personality, belief system and demographic data).

The findings highlight the importance of incorporating emotion-based variables as model input in predicting the influence of textual statements with embedded persuasion techniques. Across all investigated models, the influence effect could be predicted with an accuracy ranging 53%–70%, indicating the importance of testing multiple ML algorithms in the development of a persuasive communication (PC) system. The classification ability of models was highest when predicting the response to statements using rhetorical devices and flattery persuasion techniques. Contrastingly, techniques such as authority or social proof were less predictable. Adding DAS scale features improved model performance, suggesting they may be important in modelling persuasion.

In this study, the survey was limited to English-speaking countries and largely Western society values. More work is needed to ascertain the efficacy of models for other populations, cultures and languages. Most PC efforts are targeted at groups such as users, clients, shoppers and voters with this study in the communication context of education – further research is required to explore the capability of predictive ML models in other contexts. Finally, long self-reported psychological questionnaires may not be suitable for real-world deployment and could be subject to bias, thus a simpler method needs to be devised to gather user profile data such as using a subset of the most predictive features.

The findings of this study indicate that leveraging richer profiling data in conjunction with ML approaches may assist in the development of enhanced persuasive systems. There are many applications such as online apps, digital advertising, recommendation systems, chatbots and e-commerce platforms which can benefit from integrating persuasion communication systems that tailor messaging to the individual – potentially translating into higher economic returns.

This study integrates sets of features that have heretofore not been used together in developing ML-based predictive models of PC. DAS scale data, which relate to dysfunctional beliefs and cognitive vulnerabilities, were assessed for their importance in identifying effective persuasion techniques. Additionally, the work compares a range of persuasion techniques that thus far have only been studied separately. This study also demonstrates the application of various ML methods in predicting the influence of linguistic styles/persuasion techniques within textual statements and show that a robust methodology comparing a range of ML algorithms is important in the discovery of a performant model.