The Modern Call Center: A Multi‐Disciplinary Perspective on Operations Management Research

Call centers are an increasingly important part of today's business world, employing millions of agents across the globe and serving as a primary customer‐facing channel for firms in many different industries. Call centers have been a fertile area for operations management researchers in several domains, including forecasting, capacity planning, queueing, and personnel scheduling. In addition, as telecommunications and information technology have advanced over the past several years, the operational challenges faced by call center managers have become more complicated. Issues associated with human resources management, sales, and marketing have also become increasingly relevant to call center operations and associated academic research. In this paper, we provide a survey of the recent literature on call center operations management. Along with traditional research areas, we pay special attention to new management challenges that have been caused by emerging technologies, to behavioral issues associated with both call center agents and customers, and to the interface between call center operations and sales and marketing. We identify a handful of broad themes for future investigation while also pointing out several very specific research opportunities.     

和而不同：民族交融态度对中华民族共同体意识的影响及其心理机制

民族交融作为民族交往交流交融的关键一步,对铸牢中华民族共同体意识具有重要作用。当前学界鲜有关于个体民族交融态度如何影响中华民族共同体意识的心理学实证研究。本文采用《民族交融态度量表》《中华民族认同量表》《心理距离量表》和《中华民族共同体意识量表》,通过随机抽样的方式对682名参与者进行了实证调查。研究结果表明：民族交融态度对中华民族共同体意识具有正向预测作用,即积极的民族交融态度有利于提高个体的中华民族共同体意识水平。同时,中华民族认同和心理距离在民族交融态度与中华民族共同体意识的关系间起链式中介作用。基于此提出“保障民族交融政策有序落实,培育积极民族交融态度;正确处理共同性与差异性的关系,打造中华民族认同社会共识;充分挖掘与宣传历史记忆,重视隐喻对心理距离的作用;积极探索个体心理发展规律,鼓励多学科交叉融合研究”等建议,以期为铸牢中华民族共同体意识提供心理学实践方案。     

Narrative persuasion and psychological distance: Analyzing the effectiveness of distance-framed narratives in communicating ocean plastic pollution

Narratives have been identified as an effective tool to communicate seemingly abstract and uncertain risks. This study integrates the construal level theory of psychological distance and narrative persuasion to examine how distance-framed narratives influence young adults’ attitude, behavioral intention, and policy support related to ocean plastic pollution. Results from an experimental survey (N = 889) indicate that the narrative featuring socially close characters and spatially close location is least effective in producing persuasive effects. Instead, the narrative depicting socially close characters’ encounters at a spatially distant location is more persuasive. Within narrative conditions, the findings support the mediating role of identification and transportation in facilitating narrative effects. Serial mediation of identification/transportation and psychological distance perception was also observed. Findings from this study offer meaningful theoretical and practical implications for strategic communication on ocean plastic pollution.     

Efficient Estimation of Models with Conditional Moment Restrictions Containing Unknown Functions

We propose an estimation method for models of conditional moment restrictions, which contain finite dimensional unknown parameters (θ) and infinite dimensional unknown functions (h). Our proposal is to approximate h with a sieve and to estimate θ and the sieve parameters jointly by applying the method of minimum distance. We show that: (i) the sieve estimator of h is consistent with a rate faster than n^‐1/4 under certain metric; (ii) the estimator of θ is √n consistent and asymptotically normally distributed; (iii) the estimator for the asymptotic covariance of the θ estimator is consistent and easy to compute; and (iv) the optimally weighted minimum distance estimator of θ attains the semiparametric efficiency bound. We illustrate our results with two examples: a partially linear regression with an endogenous nonparametric part, and a partially additive IV regression with a link function.     

Focused information criterion for locally misspecified vector autoregressive models

This paper investigates the focused information criterion and plug-in average for vector autoregressive models with local-to-zero misspecification. These methods have the advantage of focusing on a quantity of interest rather than aiming at overall model fit. Any (su?ciently regular) function of the parameters can be used as a quantity of interest. We determine the asymptotic properties and elaborate on the role of the locally misspecified parameters. In particular, we show that the inability to consistently estimate locally misspecified parameters translates into suboptimal selection and averaging. We apply this framework to impulse response analysis. A Monte Carlo simulation study supports our claims.     

Staying Connected: An Examination of Relationship Maintenance Behaviors in Long-Distance Relationships

Romantic long-distance relationships (LDRs) are becoming increasingly prevalent as individuals seek educational and employment opportunities across the globe. LDRs create unique challenges for couples, potentially impacting couple and individual well-being. It is important to understand efforts that LDR partners make to stay engaged, known as relationship maintenance behaviors (RMBs), and whether common RMBs facilitate or hinder relational and individual functioning. This study has two goals: (a) to examine whether RMBs predict relationship satisfaction and (b) to investigate whether relationship satisfaction mediates the association between RMBs and individual functioning. Eighty-seven adults in LDRs participated in our online survey. Results indicated that RMBs predicted relationship satisfaction, some negatively, and that relationship satisfaction was a mediator between RMBs and individual well-being.     

Fuzzy clustering of probability density functions

Basing on L¹-distance and representing element of cluster, the article proposes new three algorithms in Fuzzy Clustering of probability density Functions (FCF). They are hierarchical approach, non-hierarchical approach and the algorithm to determine the optimal number of clusters and the initial partition matrix to improve the qualities of established clusters in non-hierarchical approach. With proposed algorithms, FCF has more advantageous than Non-fuzzy Clustering of probability density Functions. These algorithms are applied for recognizing images from Texture and Corel database and practical problem about studying and training marks of students at an university. Many Matlab programs are established for computation in proposed algorithms. These programs are not only used to compute effectively the numerical examples of this article but also to be applied for many different realistic problems.     

A robust variable screening method for high-dimensional data

In practice, the presence of influential observations may lead to misleading results in variable screening problems. We, therefore, propose a robust variable screening procedure for high-dimensional data analysis in this paper. Our method consists of two steps. The first step is to define a new high-dimensional influence measure and propose a novel influence diagnostic procedure to remove those unusual observations. The second step is to utilize the sure independence screening procedure based on distance correlation to select important variables in high-dimensional regression analysis. The new influence measure and diagnostic procedure that we developed are model free. To confirm the effectiveness of the proposed method, we conduct simulation studies and a real-life data analysis to illustrate the merits of the proposed approach over some competing methods. Both the simulation results and the real-life data analysis demonstrate that the proposed method can greatly control the adverse effect after detecting and removing those unusual observations, and performs better than the competing methods.     

Characteristic function-based inference for GARCH models with heavy-tailed innovations

We consider estimation and goodness-of-fit tests in GARCH models with innovations following a heavy-tailed and possibly asymmetric distribution. Although the method is fairly general and applies to GARCH models with arbitrary innovation distribution, we consider as special instances the stable Paretian, the variance gamma, and the normal inverse Gaussian distribution. Exploiting the simple structure of the characteristic function of these distributions, we propose minimum distance estimation based on the empirical characteristic function of properly standardized GARCH-residuals. The finite-sample results presented facilitate comparison with existing methods, while the new procedures are also applied to real data from the financial market.