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The purpose of this study is to address why managers enter the excessive market. A comparison of the facts and perceptions of entrants relative to success in the market shows that many entrants are confident about the viability of their businesses and enter the market. Accordingly, the authors simulate market entry decisions to detect behavioral biases.

The authors adapted the entry decisions simulation method, which is supported by the theoretical foundations of signal detection theory (SDT) and signaling theory. The simulation model is implemented on the Anaconda platform and written in Python 3.

The results of this study suggest that overestimation relates to excess market entry. Also, the proportion of excess entry under difficult conditions is always higher than under easy conditions.

This research helps managers and firms think about their and their competitors' abilities and evaluate them before entering the market. Policymakers and practitioners can also design programs such as experiential learning to help entrants assess their skills.

So far, no research has investigated the role of overconfidence under different market conditions. Accordingly, this study contributes to the current market entry literature by disentangling the debate between absolute and relative confidence and by considering the role of task difficulty.

The current study examines the effects of race, agency and environment on traffic stops in rural and non-rural spaces.

Using traffic stop data collected in a Midwest US County from January 1, 2020 to December 31, 2021, the current study uses logistic regression to examine racial disparities in traffic stops.

The results indicate that police decision-making in traffic stops may be influenced by other factors besides a driver’s race or ethnicity. In other words, the police officer’s decision making in a traffic stop varies between small and large agencies as well as rural and non-rural places.

This study provides one of the few examinations of racial disparities in traffic stops in rural places.

Fall accidents can cause casualties and economic losses in the construction industry. Fall portents, such as loss of balance (LOB) and sudden sways, can result in fatal, nonfatal or attempted fall accidents. All of them are worthy of studying to take measures to prevent future accidents. Detecting fall portents can proactively and comprehensively help managers assess the risk to workers as well as in the construction environment and further prevent fall accidents.

This study focused on the postures of workers and aimed to directly detect fall portents using a computer vision (CV)-based noncontact approach. Firstly, a joint coordinate matrix generated from a three-dimensional pose estimation model is employed, and then the matrix is preprocessed by principal component analysis, K-means and pre-experiments. Finally, a modified fusion K-nearest neighbor-based machine learning model is built to fuse information from the x, y and z axes and output the worker's pose status into three stages.

The proposed model can output the worker's pose status into three stages (steady–unsteady–fallen) and provide corresponding confidence probabilities for each category. Experiments conducted to evaluate the approach show that the model accuracy reaches 85.02% with threshold-based postprocessing. The proposed fall-portent detection approach can extract the fall risk of workers in the both pre- and post-event phases based on noncontact approach.

First, three-dimensional (3D) pose estimation needs sufficient information, which means it may not perform well when applied in complicated environments or when the shooting distance is extremely large. Second, solely focusing on fall-related factors may not be comprehensive enough. Future studies can incorporate the results of this research as an indicator into the risk assessment system to achieve a more comprehensive and accurate evaluation of worker and site risk.

The proposed machine learning model determines whether the worker is in a status of steady, unsteady or fallen using a CV-based approach. From the perspective of construction management, when detecting fall-related actions on construction sites, the noncontact approach based on CV has irreplaceable advantages of no interruption to workers and low cost. It can make use of the surveillance cameras on construction sites to recognize both preceding events and happened accidents. The detection of fall portents can help worker risk assessment and safety management.

Existing studies using sensor-based approaches are high-cost and invasive for construction workers, and others using CV-based approaches either oversimplify by binary classification of the non-entire fall process or indirectly achieve fall-portent detection. Instead, this study aims to detect fall portents directly by worker's posture and divide the entire fall process into three stages using a CV-based noncontact approach. It can help managers carry out more comprehensive risk assessment and develop preventive measures.

This study explores how police culture is experienced by women officers serving in positions where they are significantly underrepresented (i.e. leadership and elite specialty units) and the environmental factors that shape these experiences.

A qualitative analysis of transcripts from interviews with 71 women serving in male-dominated roles was conducted (N = 39 ranking women; N = 32 women on elite units).

Participants described five occupational, organizational and assignment-level factors that shaped their workplace experiences. While some contextual forces at play are similar for women working patrol (e.g. traditional police culture, the underrepresentation of women in law enforcement) unique position-level factors were also identified (e.g. the high-risk and consequential nature of the work).

While the cultural environment for women patrol officers has improved in the past few decades, the same cannot be said for women working in positions that are still dominated by men. More attention to this area of policing is needed to ensure gender diversity is achieved throughout organizations and not only in positions deemed suitable for women.

The study extends research on women in policing beyond the focus on patrol. Further, it explores the assignment- and rank-based perspectives of police culture, which are largely absent from the literature.

Active shooter incidents have risen considerably in recent years, elevating public and law enforcement focus on improving response to these incidents. The contemporary policy for reacting to an active shooter event is for the officers who first arrive on the scene to move quickly to engage and neutralize a shooter, prioritizing victim safety and minimizing loss of life. This study provides a preliminary understanding of the police view regarding their role in active shooter events and their experiences with active shooter training.

We conducted a survey of 413 sworn personnel across three US cities. Analyses examine differences in officers’ training experiences across the agencies and predictors of a sense of duty to prioritize victims’ lives over that of officers.

Officers’ training experiences differed by agency, yet most officers supported a duty to sacrifice their lives to prioritize victims’ safety during active shooter incidents. Officers with more years of experience have lower odds of supporting a duty to sacrifice their lives to save victims during an active shooter situation relative to less experienced officers. Respondents who recall officer safety among the top three most memorable topics from their recent active shooter training also have lower odds of supporting a duty to sacrifice.

The authors add knowledge about police officers’ experiences with active shooter training and officers’ opinions about their role in responding to active shooter incidents. We discuss implications of the findings relative to police culture and training and suggest directions for future research.

Disasters and other emergencies are increasing, with millions of people affected by events like earthquakes, fires and flooding. The use of mobile emergency alert systems (MEAS) can improve people’s responses by providing targeted alerts based on location and other personal details. This study aims to understand the factors that influence people’s willingness to share the personal information that is needed to provide context-specific messaging about a threat and protective actions.

Drawing on protection motivation theory (PMT), this study proposes and tests a model of willingness to use personalised MEAS that incorporates key factors related to an individual’s appraisal of a potential threat (i.e. perceived vulnerability and severity) and coping capacity (i.e. response efficacy and self-efficacy), with deterrents like response cost and privacy concern. This study uses survey data from 226 respondents in New Zealand and SmartPLS to assess the model.

The results show how willingness to use MEAS is influenced by people’s appraisal of an emergency threat and their perception of how using MEAS would help them to cope effectively. Fear and perceived severity are significant motivators of MEAS use, along with coping appraisal. However, when the negative influences of privacy concern and response cost are strong enough, they can dissuade use, despite knowing the risks.

The study addresses a gap in research on the use of alert systems like MEAS, which require sharing of personal information and continuous engagement such as the real-time disclosure of one’s location. It confirms the significance of factors not studied in prior research, such as privacy concerns, that can dissuade use. This study also extends the application of the PMT in the context of emergency management.

In this study, we illuminate how techniques can be incorporated into interview protocols when conducting research with educational leaders who are being asked to discuss their experiences in crises.

We interviewed seven researchers about their role as a researcher in collecting data on a crisis event from participants. Our analysis concentrated on several key components of the interview.

In presenting our findings on how scholars can adopt a caring and just approach to interview studies with leaders regarding crises, we portray how this approach can be melded into research design, interview protocol and interview techniques.

We illuminate that specific interview techniques are required when interviewing participants who have undergone and survived crises in their work, and we recommend the use of this protocol especially when an interview requires researchers to “handle with care.”

The purpose of this study is to explore the design of outdoor play spaces in Cairo that provide an enjoyable play experience, along with opportunities for enhancing child social and cognitive developmental skills through play features incorporated in their play spaces to achieve this goal.

The study adopts a qualitative methodology to examine the effectiveness of natural, customized and elevated features on social and cognitive play behaviors of 6–8 year-olds. Data were gathered in three different play settings; a play space inside a social club, a park and a schoolyard. Data gathering relied on observations, written descriptions of play patterns and recordings of children's conversations. Additionally, the researcher utilized sketching diagrams to illustrate children's preferences for play with each feature.

The results of the study indicate that incorporating natural, elevated and customized play features into children's play spaces can enhance their environment and provide opportunities for fostering their social and cognitive skills.

This study reports the occurrence of indicative behaviors and not the exact measurement of skill development. Research involving children can have limitations in terms of reliability of results due to slight variations affected by unmeasurable circumstances.

The study makes a valuable contribution towards enhancing the quality of children's play spaces in Cairo by emphasizing the significance of providing opportunities for social and cognitive in addition to physical play.

This study aims to explore customer perceived value (CPV) dimensions in the context of free mobile educational applications (EduApps) which are paramount in learning-based digital start-ups and are essential for the implementation of circular economy (CE). The purpose of the present study is to identify dimensions of CPV specifically for EduApps and propose a conceptual model that would assist the digital start-up decisions which in turn can be a catalyst in navigating to a CE.

The study uses the Netnography approach by analyzing online user-generated content. A total of 13,147 reviews posted on the Google play store after using top free education apps were coded using ATLAS.ti 9 software.

Major dimensions of context-specific CPV are identified as technical value, content value, pedagogical value, gamification value and learning value. Subdimensions and items are extracted for each of these dimensions.

The larger subscriber base drives sponsorships, advertisements and donations which underpin the business model of free EduApps. This can be obtained through an attractive value proposition. Identifying context-specific value dimensions would aid entrepreneurs in optimal value mix development decisions. The proposed framework can be utilized by both researchers (for scale creation, comparative studies and quantitative studies) and practitioners (for entrepreneurial decisions on better value propositions).

CPV successfully describes consumer decision-making, but less attention is paid to linking the theory to the setting of mobile learning apps, where the bulk of research is focused on techniques like TAM, UTAUT, etc. In addition, studies identifying CPV from mobile apps with a specific focus on EduApps are sparse. Extant literature in this context is either based on a foundation of in-store business value dimensions or dominated by technical aspects when focused on the context of mobile apps. The current study bridges this gap.

Determining the impact of behavioral influences on the stock market has significant implications for investment analysis and portfolio management. Behavioral biases are parameters that need to be considered in investment decision-making. The purpose of this study is to inform Bangladeshi investors about behavioral biases that they may encounter when making investment decisions in the prevailing frontier environment.

Through the chi-square test, one-way ANOVA, paired-samples t-test and descriptive analysis based on the facts collected from 281 respondents of the Dhaka Stock Exchange (DSE), the study has found that individual investors of Bangladesh often make investment decisions emotionally rather than based on theories.

The result shows that risk aversion and risk perception are the two most influential emotional dimensions that impact investors' decisions. The findings are consistent with the other researchers and highlight the fact that investors hardly act according to the norms recommended in the financial theories.

The findings are grounded on a small portion of investors at DSE on some particular days, which is not sufficient to study individual investors' entire complex decision-making behavior from various angles. Many respondents were reluctant and even confused to disclose their behavioral aspects. These, along with biased and careless answers, may impede the identification of the actual scenario of the behavioral responses in decision-making that demand further study.

The novelty of this study is unique in that it examined investors of the DSE, who are considered to be a representative in a frontier market like Bangladesh. Since this market is not very resilient, small investors need to be aware of the biases of behavioral factors to survive.