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Despite its flourishing development since first proposed, job crafting literature has provided limited insights into why people craft their jobs. This study theoretically develops a two-dimensional integrative framework for the motives of job crafting, including orientation (self-oriented vs work-oriented vs other-oriented) and self-determination (autonomous vs introjected vs external) dimensions. We further investigate the specific motives of job crafting from actor and observer perspectives.

We conducted two critical-incident recall surveys among 120 and 100 employees from varied sectors and organizations, who responded from the actor and observer perspective respectively. 395 and 299 valid open-ended responses were then collected and coded following the steps for content analysis.

Drawing from the proposed two-dimensional theoretical framework, we identified 16 specific job crafting motives from actor and observer perspectives.

Our findings remind managers to pay attention to employees' motives of job crafting and take appropriate managerial actions according to their varied motives.

By incorporating job crafting from the motivation literature and identifying diversified motives that drive employees to engage in job crafting, this qualitative study contributes to both the job crafting literature and the broader application of self-determination theory in the field of organizational behavior.

Taking attribution theory as an overarching framework, the study aims to examine how employees attribute and respond to a colleague's approach crafting.

Two complementary studies, including a scenario experiment (Study 1; N = 114) and an online survey (Study 2; N = 220), were conducted to test the hypothesized model.

Study 1 found support for the attribution of a prosocial motive to approach crafting, which in turn led to more social support and less social undermining among observers. This mediation was stronger when the job crafter was perceived as less other-oriented. Study 2 replicated the findings of Study 1 and further showed that when observers attributed both high impression management and prosocial motives to approach crafting, the positive relationship between their prosocial motive attribution and social support for the job crafter got weakened, while the negative relationship between their prosocial motive attribution and social undermining of the job crafter was strengthened.

The findings demonstrate that approach crafting gives rise to specific attributions and reactions toward the job crafter, which enrich the understanding of the social consequences of job crafting in the workplace.

This study aims to analyze government hotline text data and generating forecasts could enable the effective detection of public demands and help government departments explore, mitigate and resolve social problems.

In this study, social problems were determined and analyzed by using the time attributes of government hotline data. Social public events with periodicity were quantitatively analyzed via the Prophet model. The Prophet model is decided after running a comparison study with other widely applied time series models. The validation of modeling and forecast was conducted for social events such as travel and educational services, human resources and public health.

The results show that the Prophet algorithm could generate relatively the best performance. Besides, the four types of social events showed obvious trends with periodicities and holidays and have strong interpretable results.

The research could help government departments pay attention to time dependency and periodicity features of the hotline data and be aware of early warnings of social events following periodicity and holidays, enabling them to rationally allocate resources to handle upcoming social events and problems and better promoting the role of the big data structure of government hotline data sets in urban governance innovations.

Leader–member exchange (LMX) relationship is a crucial context for individuals’ generation of creative ideas. Unlike the fruit research between LMX quality and employee creativity, the relationship between LMX ambivalence and employee creativity is scarce. This study thus aims to examine the effect of LMX ambivalence on employee creativity.

This study conducted an on-site survey of research and development teams in China and distributed paired questionnaires with a time lag of one month. Data from 116 leaders and 484 subordinates were collected and analyzed.

The results showed that LMX ambivalence was directly negatively or indirectly negatively related to employee creativity via self-efficacy. Employee cynicism acted as a potential personal moderator of the effect of LMX ambivalence, specifically, employee cynicism attenuated the negative influence of LMX ambivalence on employee creativity via self-efficacy.

This study extends our knowledge of the complex effects of LMX relationships by empirically exploring whether and how LMX ambivalence influences employee creativity, with self-efficacy introduced as one crucial underlying mechanism. Meanwhile, this study enriches the existing cynicism literature by demonstrating the role of employee cynicism as a buffer in the relationship between LMX ambivalence and employee creativity.

New generation of construction workers (NGCWs) who were born in the 1980s and later have gradually become the main workforce of Chinese construction industry. They may behave differently when dealing with knowledge-related activities due to divergent characteristics caused by generational discrepancy. To provide a theoretical foundation for construction companies and safety managers to improve safety management, this research explores the factors and paths impacting the NGCWs' ability to share their safety knowledge.

Based on literature review, main factors that influence the safety knowledge sharing of the NGCWs were identified. Decision-Making Trial and Evaluation Laboratory and Interpretive Structural Modeling were applied to identify the hierarchical and contextual relations among the factors influencing the safety knowledge sharing of the NGCWs.

The results showed that sharing atmosphere ranked first in centrality and had a high degree of influence and being influenced, indicating itself an extremely important influencing factor of safety knowledge sharing of NGCWs. Six root influencing factors were identified, including individual characteristics, work pressure, sharing platform, incentive mechanism, leadership support and safety management system.

The number of influencing factors of safety knowledge sharing of the NGCWs identified in this study is limited, and the data obtained by the expert scoring method is subjective. In future studies, the model should be further developed and validated by incorporating experts from different fields to improve its integrity and applicability.

The influencing factors identified in this paper can provide a basis for construction companies and safety managers to improve productivity and safety management by taking relevant measures to promote safety knowledge sharing. The research contributes to the understanding knowledge management in the context of the emerging market. It helps to answer the question of how the market can maintain the economic growth success through effective knowledge management.

This paper investigates the influencing factors of NGCWs' safety knowledge sharing from the perspective of intergenerational differences, and the 13 influencing factor index system established expands the scope of research on factors influencing safety knowledge sharing among construction workers and fills the gap in safety knowledge sharing research on young construction workers. Furthermore, this paper establishes a multi-layer recursive structure model to clarify the influence path of the influencing factors and contributes to the understanding of safety knowledge sharing mechanism.

Green innovation (GI) in megaprojects has become a significant research topic that attracts both megaproject management scholars' and practitioners' attention. Green transformational leadership (GTL) is acknowledged as an important antecedent to GI in the permanent context. However, limited research investigates the mechanism and condition of how GTL effectively affects GI in the temporary (i.e. megaproject) context. This study seeks to examine the mechanism and condition of GTL in improving GI by assessing the mediating role of green knowledge sharing (GKS) and the moderating effect of innovation climate (IC).

Regression analysis was performed on data obtained from 303 experts who have been involved in megaprojects.

GTL has a significant positive impact on two aspects of GI, including green product innovation (GPDI) and green process innovation (GPCI). Besides, GKS mediates the relationship between GTL and the two aspects of GI. Moreover, IC plays a significantly positive moderating role in the relationship between GTL and GKS and the relationship between GKS and the two aspects of GI.

This study adds knowledge to the theory and practice by unveiling the “black box” between GTL and GI in the temporary (i.e. megaproject) context. First, this study extends the continuing discussion on the direct effect of GTL on GI to the temporary (i.e. megaproject) context. Second, this study facilitates the understanding of the mechanism to generate better GI performance considering the mediating role of GKS and the moderating effect of IC in the temporary (i.e. megaproject) context. The results can illuminate megaproject practitioners on generating better GI performance.

The purpose of this paper is to identify the Nomoto ship model parameters accurately, in order to produce a very close match between the predictions based on the model and the full‐scale trials.

Various ship maneuvering mathematical models have been used when describing the ship dynamics behavior. The Nomoto ship model is a class of simplified hydrodynamic derivative type models which are the most widely used, accepted and perhaps well developed. To determine the model parameters accurately, particle swarm optimization (PSO) is chosen as an evolution algorithm in this paper. This arithmetic can guarantee the convergence and global optimization ability, and avoid sinking into a local optimal solution.

The process of PSO for identifying the Nomoto ship model parameters is given.

Availability of the full‐scale trial data are the main limitations.

The ship model parameters provide very useful advice in ship's autopilot process.

The paper presents a new parameter identification method for the second‐order Nomoto ship model based on PSO.

In the presence of coronavirus disease 2019 (COVID-19), due to the social distance restriction, consumers' regular consumption behaviors and patterns have been changing fundamentally. Thereafter, an innovative group buying model has emerged and developed explosively with a specific focus on consumer's location, known as community-based group buying (CGB). The purpose of this paper is to investigate the transfer mechanism of user's trust in dyadic contexts of social and commercial role-playing in the CGB program.

This study adopts an empirical research method, with an online and offline questionnaire survey, a total of 382 responses have been obtained. Then, both descriptive analysis and hierarchical regression analysis are conducted to explore the dual roles of group leader and its corresponding effects on consumers' trust (i.e. emotional trust and behavioral trust) and engagement actions (i.e. purchase and share) in the CGB program.

Results indicate that resident's trust and their perception of group leader's friend role can positively enhance their engagement actions in the CGB programs. Meanwhile, for the purpose of profit maximization, the group leader is more willing to play a friend role in transactions no matter whether the role conflict exists.

Research findings provide some managerial insights for CGB platform on the selection and training of group leaders and the incentive mechanism design.

This paper aims to investigate actual tourist customer visiting behavior with behavioral data from Google Popular Times to evaluate the extent that such an online source is useful to better understand, analyze and predict tourist consumer behaviors.

Following six hypotheses on tourist behavior, a purpose-built software tool was developed, pre-tested, and then used to obtain a large-scale data sample of 20,000 time periods for 198 restaurants. Both bi-variate linear regression and correlation analyzes were used for hypothesis testing.

Support was established for the hypotheses, through an analysis of customer reviews, timing effects, the number of pictures uploaded and price segment information provided by tourists to a given restaurant. Also, a relationship to average duration time was found to be positive. The findings demonstrate that data provided through Google Popular Times matches theoretical and logical assumptions to a high degree. Thus, the data source is potentially powerful for providing valuable information to stakeholders (e.g. researchers, managers and tourists).

This paper is the first to both conceptually and empirically demonstrate the practicality and value of Google Popular Times to better understand, analyze and predict tourist consumer behaviors. Value is thereby provided by the potential for this approach to offer insights based behavioral data. Importantly, until now such an approach to gathering and analyzing this volume of actual customer data was previously considered far less practical in terms of time and expense.

本研究旨在用谷歌热门时段(Google Popular Times)的行为数据来探讨游客的实际访问行为, 以评估此种线上资源对理解、分析和预测游客消费行为的实用程度。

基于对游客访问行为的六个假设, 本研究开发并前测一种专用软件工具, 用其收集198家餐厅中20,000个时间段的大规模数据样本。双变量线性回归(bi-variate linear regression)和相关性分析(correlation analyzes)均用于检验假设。

研究结果支持本文假设, 包含顾客评论数量、时间影响、图片数量以及价格区间等资讯對特定餐厅游客平均数量之預測; 同时亦发现与平均持续时间的正向关系。研究结果证明, 谷歌热门时段所提供的数据很大程度上符合理论与逻辑假设。因此, 其具备潜在强大功能, 能为利害关系人（如研究者, 管理者, 游客）提供高价值的资讯。

本研究是第一个从概念与实证上证明谷歌热门时段的实用性和价值, 进而深入理解、分析和预测游客消费行为。此方式透过行为数据来提供深入的见解并创造价值; 重要的是, 在此之前, 这种收集与分析大量实际顾客数据的方法被认为缺乏时间与成本效益。

El presente documento tiene como objetivo investigar las conductas de visita de los clientes turísticos reales con datos de comportamiento de Tiempos populares de Google (Google Popular Times) para evaluar el grado en que dicha fuente online es útil para comprender, analizar y predecir mejor las conductas de los consumidores turísticos.

Siguiendo seis hipótesis sobre el comportamiento de los clientes turístico visitante, se desarrolló una herramienta de software especialmente diseñada, probada con anterioridad y posteriormente se utilizó para obtener una muestra de datos a gran escala de 20.000 períodos de tiempo para 198 restaurantes. Se utilizaron tanto la regresión lineal bi-variante como los análisis de correlación para probar las hipótesis.

Se apoya la hipótesis que incluyen la cantidad de comentarios de los clientes, los efectos de tiempo, el número de imágenes y la información del segmento de precios sobre la cantidad de turistas que visitan un restaurante determinado en promedio. Además, se encuentra una relación positiva con el tiempo de duración promedio. Los hallazgos demuestran que los datos proporcionados a través de Google Popular Times coinciden en alto grado con las suposiciones teóricas y lógicas. Por lo tanto, la fuente de datos es potencialmente eficaz para proporcionar información valiosa a los interesados (por ejemplo, investigadores, administradores, turistas).

Este ensayo es el primero que demuestra conceptual y empíricamente la practicidad y el valor de Google Popular Times para entender, analizar y predecir mejor el comportamiento del consumidor turístico. Por lo tanto, el valor es proporcionado por el potencial de este enfoque para ofrecer datos de comportamiento basados en la comprensión. Es importante señalar que hasta ahora ese enfoque para reunir y analizar ese volumen de datos reales sobre los clientes se consideraba menos práctico en términos de tiempo y gastos.