城市土地利用与交通相互作用（LUTI）研究进展与展望

研究目的:基于对国内外大量研究成果的梳理,对城市土地利用与交通相互作用(LUTI)的复杂机理展开系统回顾与述评,并尝试提出未来亟需重点关注的研究领域与方向。研究方法:文献资料法,对比分析法。研究结果:国内外实证研究领域及空间尺度表现出明显分异,部分研究结果亦呈现出差异化特征;国外热衷于引入多学科理论与方法构建LUTI一体化模型,而国内所引入的数学规划模型多停滞在理论分析与模拟层面,应用性相对有限,但近年来国内学者逐渐加强LUTI协调性评价,研究领域呈现出新特征。研究结论:全球可持续发展背景下,LUTI研究为实现城市土地利用与交通的协调发展提供了良好基础,未来应加强对LUTI模型方法与互馈机理的研究,并深入探讨现代交通发展对土地利用的影响机制,环境、社会因素亦应引起重视,国内学界则应注重本土性与应用性研究。     

Interorganisational project dynamics: A longitudinal study of perceptual distance and client-contractor collaborative relationships

Despite the importance of good collaborative relationships in interorganisational projects, clients and contractors often develop adversarial relationships due to perceptual distance about key project issues. In this case study research, we investigated how perceptual distance emerges and changes over time, and how the collaborative relationship between client and contractor develops alongside these dynamics. In this exploration, we built upon agency theory and stewardship theory as complementary perspectives for understanding client-contractor collaborative relationships. We gathered quantitative and qualitative data in two projects, conducting three assessments in about one year. We found that perceptual distance increased and decreased over time, and that a reduction was typically associated with the collaborative relationship being characterized by stewardship rather than agency. These findings suggest that a regular assessment and evaluation of partners’ perceptions of critical project issues is warranted to timely detect and counteract perceptual distance. Moreover, partners would best adopt a stewardship orientation to reduce perceptual distance, although this may take considerable effort given the distributive nature of many pre-project negotiations.     

Alternative balanced scorecards built from paradigm models in strategic HRM and employment/industrial relations and used to measure the state of employment relations and HR system performance across U.S. workplaces

This paper constructs alternative balanced scorecards based on high‐performance work system (HPWS) and employment relations system (ERS) models. The models are depicted and compared in diagrams and used as framework skeletons for building separate HPWS and ERS scorecards, intended to provide a detailed data picture of the operational health and performance of an organization's employment/HR system and its operations, processes, and inputs/outputs. The scorecards are filled in with nationally representative data from 2,000+ U.S. workplaces using more than 50 employment/HR indicators, as reported by separate panels of managers and employees. The indicators for each workplace are aggregated into an overall HR/employment system score, ranked from low‐to‐high, and graphed as frequency distributions. These distributions provide a unique snapshot picture of the mean and dispersion of the state of employment relations and HR system performance for companies across the United State. They also reveal that “models matter” since the HPWS and ERS scorecards provide distinctly different evaluation assessments.     

Examining artificial intelligence (AI) technologies in marketing via a global lens: Current trends and future research opportunities

Artificial intelligence (AI) has captured substantial interest from a wide array of marketing scholars in recent years. Our research contributes to this emerging domain by examining AI technologies in marketing via a global lens. Specifically, our lens focuses on three levels of analysis: country, company, and consumer. Our country-level analysis emphasizes the heterogeneity in economic inequality across countries due to the considerable economic resources necessary for AI adoption. Our company-level analysis focuses on glocalization because while the hardware that underlies these technologies may be global in nature, their application necessitates adaptation to local cultures. Our consumer-level analysis examines consumer ethics and privacy concerns, as AI technologies often collect, store and process a cornucopia of personal data across our globe. Through the prism of these three lenses, we focus on two important dimensions of AI technologies in marketing: (1) human–machine interaction and (2) automated analysis of text, audio, images, and video. We then explore the interaction between these two key dimensions of AI across our three-part global lens to develop a set of research questions for future marketing scholarship in this increasingly important domain.     

Evaluating discounts as a dimension of customer behavior analysis

Today, increased competition between organizations has led them to seek a better understanding of customer behavior through identifying valuable customers. Customers’ expectations about the price and quality of products and services play an important role in their selection process. In online businesses, competition and price differences between suppliers is high, so discounts will attract different customers. As a result, discounts and the frequency and amount of purchases can lead to better understanding of customer behavior. Customer segmentation and analysis is essential for identifying groups of customers. Hence, this study uses a model based on RFM called RdFdMd, in which d is the level of discount used to analyze customer purchase behavior and the importance of discounts on customers’ purchasing behavior and organizational profitability. The CRISP-DM and k-mean algorithm were used for clustering. The results indicate that using the R_dF_dM_d model achieves better customer clustering and valuation, and discounts were identified as an important criterion for customer purchases.     

Nicknames as identity badges: How self-reflective nicknames can facilitate users’ online social interactions

An Internet nickname is essential in facilitating online interpersonal interactions. It is a pivotal element of interaction that provides other users with an initial impression and affects the decisions and behaviors of users during their online interactions. Although the existing literature has found that nicknames are relevant to users’ psychology and behavior, direct research on the motivation for and the effect of choosing a particular Internet nickname has been very limited. Borrowing from relevant theories on avatars, this paper summarizes the motivations behind Internet nickname choices in terms of three aspects: virtual exploration, social navigation and contextual adaptation. Moreover, from the perspective of collective self-esteem (CSE) and group identification, this research explores the influence mechanism between the motivations underlying Internet nickname choices and users’ online social interactions. A total of 394 samples were obtained from online communities in China to test our research model. We found that virtual exploration, social navigation and contextual adaptation are three motivations for creating Internet nicknames, and they have direct effects on the online social interactions of users. CSE has a full mediating effect between virtual exploration and online social interaction and acts as a mediated variable between social navigation and online social interaction. Group identification fully mediates the relationship between social navigation and online social interaction as well as that between contextual adaptation and online social interaction. The conclusion of this paper provides not only a new perspective for the study of Internet nicknames but also a potentially feasible way to stimulate users’ online social interactions.     

A multivariate distance nonlinear causality test based on partial distance correlation: a machine learning application to energy futures

This paper proposes a multivariate distance nonlinear causality test (MDNC) using the partial distance correlation in a time series framework. Partial distance correlation as an extension of the Brownian distance correlation calculates the distance correlation between random vectors X and Y controlling for a random vector Z. Our test can detect nonlinear lagged relationships between time series, and when integrated with machine learning methods it can improve the forecasting power. We apply our method as a feature selection procedure and combine it with the support vector machine and random forests algorithms to study the forecast of the main energy financial time series (oil, coal, and natural gas futures). It shows substantial improvement in forecasting the fuel energy time series in comparison to the classical Granger causality method in time series.     

Moderation by goal commitment of the feedback–performance relationship: Theoretical explanation and preliminary study

This paper presents goal commitment as one stage of a multidimensional goal striving process that affects feedback acquisition, processing, and use during the achievement of performance goals. Specifically, this paper argues that higher goal commitment leads to more effective acquisition, processing, and use of feedback that in turn fosters higher performance than does lower goal commitment. To provide a preliminary test of this proposition, data from an earlier field study conducted by the author and colleagues [J. Occup. Organ. Psychol. 72 (1999) 107] were used to examine the moderating effects of goal commitment on the relationship between perceived task feedback amount and work performance of 196 rehabilitation counselors. Results were consistent with this proposed interaction, as perceived task feedback amount had a positive linear relationship with work performance for higher goal commitment rehabilitation counselors and a negative linear relationship with performance for lower goal commitment counselors. The discussion urges researchers to broaden their views of goal commitment beyond the goal difficulty–performance relationship and to devote more attention to moderators of the feedback–performance relationship. Directions for future research and practical implications are also discussed.