Digital technologies have recently become more advanced, allowing for the development of social networking sites and applications. Despite these advancements, phone calls and text messages still make up the largest proportion of mobile data usage. It is possible to study human communication behaviors and mobility patterns using the useful information that mobile phone data provide. Specifically, the digital traces left by the large number of mobile devices provide important information that facilitates a deeper understanding of human behavior and mobility configurations for researchers in various fields, such as criminology, urban sensing, transportation planning, and healthcare. Mobile phone data record significant spatiotemporal (i.e., geospatial and time-related data) and communication (i.e., call) information. These can be used to achieve different research objectives and form the basis of various practical applications, including human mobility models based on spatiotemporal interactions, real-time identification of criminal activities, inference of friendship interactions, and density distribution estimation. The present research primarily reviews studies that have employed mobile phone data to investigate, assess, and predict human communication and mobility patterns in the context of crime prevention. These investigations have sought, for example, to detect suspicious activities, identify criminal networks, and predict crime, as well as understand human communication and mobility patterns in urban sensing applications. To achieve this, a systematic literature review was conducted on crime research studies that were published between 2014 and 2022 and listed in eight electronic databases. In this review, we evaluated the most advanced methods and techniques used in recent criminology applications based on mobile phone data and the benefits of using this information to predict crime and detect suspected criminals. The results of this literature review contribute to improving the existing understanding of where and how populations live and socialize and how to classify individuals based on their mobility patterns. The results show extraordinary growth in studies that utilized mobile phone data to study human mobility and movement patterns compared to studies that used the data to infer communication behaviors. This observation can be attributed to privacy concerns related to acquiring call detail records (CDRs). Additionally, most of the studies used census and survey data for data validation. The results show that social network analysis tools and techniques have been widely employed to detect criminal networks and urban communities. In addition, correlation analysis has been used to investigate spatial–temporal patterns of crime, and ambient population measures have a significant impact on crime rates.

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A Systematic Review of Mobile Phone Data in Crime Applications: A Coherent Taxonomy Based on Data Types and Analysis Perspectives, Challenges, and Future Research Directions

This paper theoretically analyzes and empirically examines the impact and mechanisms of automated machines on employment in manufacturing enterprises, drawing on task-based model and using micro data from listed Chinese manufacturing enterprises between 2012 and 2019. Our findings reveal that: (1) Automated machines in manufacturing enterprises leads to a substitution effect on the total labor force, with a substitution effect on low-skilled labor and a creation effect on high-skilled labor in terms of employment structure. (2) Further analysis indicates that automated machines primarily have a positive effect on R&D and technical staff, a non-significant effect on sales staff, and a negative impact on production, administrative, and financial staff. (3) The primary influencing mechanisms of automated machines on employment in manufacturing firms are productivity effects and output scale effects, based on the mediation effect model. (4) Considering the industry linkage effect, we employ the input-output method and the Input-Output Table and find that automated machines for upstream (downstream) manufacturing enterprises will result in a substitution effect on employment for downstream (upstream) enterprises. The novelties and research contributions are as follows: (1) we conduct a structural decomposition of total employment, and further decompose employment positions into production, R&D, sales, finance, and administration. (2) We try to investigate the industry linkage effect about the impact of automated machines on the employment of upstream and downstream enterprises. (3) We use data from listed manufacturing companies, and the data of existing research are about provincial and industry-level data.

How automated machines influence employment in manufacturing enterprises?


 Many professional managers are driven to achieve the bottom line to secure income, honor, and standing in the workplace. Drawing on social information processing theory, we propose that supervisor bottom-line mentality in the workplace has a dysfunctional effect on organizations. Specifically, supervisor bottom-line mentality will hinder subordinates’ perception of the meaning of work, which eventuates high employee withdrawal (turnover intention and work withdrawal behavior). We also verified that amoral management weakens the negative relationship between supervisor bottom-line mentality and meaning of work in the first-stage moderated mediation model. Additionally, the strength of the indirect effects of supervisor bottom-line mentality on turnover intention and work withdrawal behavior is weaker (stronger) when supervisors’ amoral management is high (low). Our hypothesized moderated mediation model is supported by 301 data points generated by a three-stage full-time staff member. Furthermore, we put forward important theoretical and practical implications according to the research.

Escaping the organization: Examining the influence of supervisor bottom-line mentality on employee withdrawal

Purpose Drawing on cognitive-affective personality system (CAPS) theory, this study proposes a chained multi-mediation model to examine the impact of talent management practices on talents’ intention to stay from the integration of cognitive perspective and affective perspective. Design/methodology/approach Three-wave data collected from 268 talents of Chinese organizations supported the research model. Hierarchical regression analysis was used to test the direct effects and the Bootstrap method was used to test the chain multi-mediation effects. Findings (a) Talent management practices positively affect talents’ intention to stay. (b) Perceived overqualification and perceived no growth mediate the relationship between talent management practices and talents’ intention to stay from a cognitive perspective. (c) Affective commitment mediates the relationship between talent management practices and talents’ intention to stay from an affective perspective. (d) “Perceived overqualification-affective commitment” and “perceived no growth-affective commitment” act as chain mediators between talent management practices and talents’ intention to stay, with the latter showing a stronger effect. Originality/value This study provided a comprehensive framework that examines the relationship between talent management practices and talents’ intention to stay from cognitive and affective perspectives. It contributes to deepen the understanding of the effectiveness of talent management practices and offer valuable management instructions for organizations to retain talents. 

The influence of talent management practices on talents’ intention to stay: an empirical study from China

Purpose This study delves into the intricate interplay between perceived overqualification, job search behavior, psychological well-being, and proactive behavior, within two distinct and diverse work settings. Methods Drawing upon the Person-Job Fit theory, we investigated these dynamics in two unique samples: Sample 1 encompassed corporate sector employees in the United Arab Emirates (N=409), while Sample 2 comprised IT sector workers in Pakistan (N=337). Hayes PROCESS macro were used to examine the proposed hypotheses and AMOS (Version 28) were conducted to examine model fitness. Results In Study 1, we established a positive association between perceived overqualification and job search behavior among employees in the UAE corporate sector. Notably, this relationship was mediated by psychological well-being, suggesting that the impact of perceived overqualification on job search behavior is, in part, channeled through its effects on individuals’ psychological well-being. Study 2 showed that proactive behavior exhibited a moderating effect on the negative link between perceived overqualification and psychological well-being. Specifically, employees displaying higher levels of proactive behavior demonstrated a less adverse influence of perceived overqualification on psychological well-being. Importantly, this adaptive effect of proactive behavior was found to indirectly influence job search behavior. Discussion The findings highlight the nature of perceived overqualification in the workplace and its varying impact on employee behavior and well-being across different cultural and work settings. The mediation by psychological well-being and moderation by proactive behavior in these relationships underscores the importance of individual responses to perceived job fit issues. These insights are crucial for understanding employee behavior in diverse work environments and can inform practices for managing perceived overqualification.

Cross Culture Examination of Perceived Overqualification, Psychological Well-Being and Job Search: The Moderating Role of Proactive Behavior

This paper studies the impact of a recent increase in the ratio of automated machines to ordinary capital (RAMOC) on the bias of technical change in the manufacturing industry and the mechanism influencing this. Using panel data of A-share listed manufacturing companies on the Shanghai and Shenzhen stock exchanges from 2012 to 2019, combined with the Xtfrontier model and trans-log production function, we measure the index of the bias of technical change of the manufacturing industry in China. Furthermore, we adopt a fixed effects model to test the impact of an increase of investment in automated machines on the bias of technical change. We also use an intermediary effect model to examine the intermediate mechanism from the perspectives of capital and skill matching. The results show that technical change in the manufacturing industry is biased toward automated machine capital. An incremental increase in RAMOC leads to technical change in the manufacturing industry becoming biased toward automated machine capital, wherein the intermediary mechanism is the labor structure effect. Based on industrial linkage, the investment in automated machines in the upstream (downstream) manufacturing industry increases, the technical change of the downstream (upstream) manufacturing industry is biased toward automated machine capital, and the forward linkage effect is greater than the backward linkage effect. This research enhances understanding of (1) the direction and characteristics of technical change in China, (2) how to improve the output efficiency of automated machines, (3) differences in factor revenue distribution, and (4) how new growth points in the economy can be cultivated. They show that we should encourage and support investment in automated machines, vigorously promote technical change to bias toward automated machine capital, improve the skill level of the labor force, and strengthen the match between automated machines and labor.

https://www.frontiersin.org/articles/10.3389/fpsyg.2022.874820/pdf

Investment Behavior Related to Automated Machines and Biased Technical Change: Based on Evidence From Listed Manufacturing Companies in China

Based on the power basis theory, this study examined the relationship between high-level talents’ perceived overqualification (POQ) and withdrawal behavior and the mediating role of sense of power. We also analyze the boundary effects of protected values and being trusted. The hypotheses of this study were tested through questionnaires gathered across three phases over 3 months from 371 high-level talents from 6 enterprises, 5 governments, and 13 universities in China. Hierarchical regression analyses and bootstrapping appraisals showed that: (1) POQ has a positive relationship with withdrawal behavior; (2) sense of power mediates the relationship between overqualification and withdrawal behavior, with high POQ resulting in low perceived power, which then reinforces withdrawal behavior; (3) the negative relationship between POQ and sense of power is stronger for high-level talents with high protected value (as opposed to low); (4) the negative relationship between sense of power and withdrawal behavior is stronger for high-level talents with highly being trusted (as opposed to low); (5) moderated-mediation analyses reveal that the mediating effects of sense of power are stronger for employees with high (as opposed to low) protected values or being trusted. At the end of this study, the theoretical and practical implications are discussed.

https://www.frontiersin.org/articles/10.3389/fpsyg.2022.921627/pdf

High-level talents’ perceive overqualification and withdrawal behavior: A power perspective based on survival needs

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Investment Behavior Related to Automated Machines and Biased Technical Change: Based on Evidence From Listed Manufacturing Companies in China

High-level talents’ perceive overqualification and withdrawal behavior: A power perspective based on survival needs