浅析中国石化国际化经营

面对经济全球化的浪潮,中国企业要想生存和发展,必须掌握和运用国际化经营理论,研究跨国公司的经营方式,积极拓展国际市场。中国石化集团公司,作为中国特大型企业和国家支柱产业,更应走在国际化经营前列,与跨国公司在竞争中发展,方能立于不败之地。     

实现我国跨国公司与跨国银行的协同发展

跨国公司是资本国际化,特别是金融资本国际化的必然结果,因此,企业的跨国经营离不开金融业的支持。跨国公司的发展必须有跨国银行的充分发展来支持,跨国银行的发展程度又制约着跨国公司的发展水平,这一点已被许多国家跨国公司与跨国银行的发展事实所证实。在我国跨国公司迅速发展的形势下,我国金融业如何采取协同战略,通过跨国银行的建立和发展来支持企业的跨国经营,就成为一个现实的经济问题。 一、跨国公司与跨国银行是紧     

战略联营：中国企业国际化经营的新思路

我国企业的国际化经营已进入新的阶段,通过借鉴世界一流跨国公司的成功经验,我国优秀中选择多种形式的战略联营来发展跨国经营,我国企业的国际化经营能否后来居上,成功的战备联营将成为关键因素。     

浅析跨国公司的本土化战略及其实施

本文主要介绍了跨国公司本土化的定义和内涵,接着分析跨国公司为何推行本土化战略,并分析了跨国公司的本土化常用战略及其实施手段,然后分析中国企业国际化现状,找出中国企业跨国经营中本土化的问题,最终给出跨国公司本土化经营的四点建议,即搞好跨文化沟通,加强品牌建设与管理,有效整合资源优化资源配置,制定可行的跨国经营战略规划。最后根据全文分析得出结论,中国企业跨国经营要借鉴国外跨国公司的经营战略,并结合我国实际情况,做出适合企业长期稳定发展的战略。     

当代跨国公司的发展趋势及对中国企业国际化经营的启示

随着经济全球化的发展,跨国公司已经逐渐成为全球经济发展的主动力,其对外投资决策不但对该企业的整体发展具有深刻影响,而且影响到东道国多种产业的发展道路。本文通过分析当代跨国公司发展的新趋势,指出中国企业国际化过程中碰到的问题,最后针对中国跨国企业面临的问题提出对策建议,以实现中国企业的国际化经营,走出自己跨国经营之路。     

中国大企业跨国发展的机遇与挑战——“加快培育中国跨国公司”专题会议纪要

为了总结企业的成功经验,更好地推动我国企业的国际化经营,"2012中国企业500强发布暨中国大企业高峰会"会议期间专门安排了"加快培育中国跨国公司"专题会议。各位嘉宾结合企业自身的实际和长期的研究成果,重点讨论了我国企业开展国际化经营,进行海外投资并购取得的成效与经验,分析了我国跨国公司与世界一流企业相比存在的差距,探讨了国际经济深     

并购:当代西方跨国公司发展新趋势

经过几十年的国际化经营，跨国公司越来越呈现生产国际化，经营多元化，交易的内部化和决策全球化的特点。步入20世纪90年代以后，并购是其经营战略的一大趋势，以供我们借鉴。     

我国中小企业开展国际化经营的优势

长期以来．人们在讨论企业跨国经营或国际化经营之类问题时，首先想到的是大企业、巨型跨国公司，而往往忽略了中小企业。其实，中小企业开展馆际化经营也有其优势。本文结合我国实际．重点分析了我国中小企业开展国际化经营的有利条件。     

中小企业如何应对跨国采购

基于经济全球化加速发展,跨国公司和国际化企业寻求全球扩张,以及最大限度利用全球优势资源的内在要求,跨国采购成为跨国公司、国际化企业获得竞争优势的一个重要途径。因为,采购活动是企业在经营活动中最大的成本领域,采购质量的优劣与效率的高低,很大程度地决定着企业最终产品的价值和竞争力。     

集群内跨国公司的当地结网与中小企业的国际化

针对跨国公司参与集群中当地中小企业迅速国际化的现象,文章基于演进理论与网络理论的分析,指出集群中的跨国公司为区内中小企业吸引、培养和输送了大量具有国际经营理念、知识和经验的高级人才;并且为当地企业提供了国际商务网络的共享与支持,因而大大加速了当地企业国际化进程。文章分析了对我国中小企业国际化的启示。     

Organizational Learning and CRM Success: A Model for Linking Organizational Practices,Customer Data Quality,and Performance

A high quality customer database is a cornerstone of successful interactive marketing strategies and tactics. Based on the notion that customer data quality is not only a technical but also an organizational problem, this study develops and tests an organizational learning framework of the relationship between organizational processes, customer data quality and firm performance. The findings show that high quality customer data impact both customer and business performance and that the most important driver of customer data quality comes from the executive suite. A large portion of the impact of organizational culture on performance is mediated by customer data quality and data sharing. The results support the presence of a hierarchy of effects for enhancing data quality that runs from organizational learning (committed to a shared vision for CRM data), to cross-functional learning (marketing/IT cooperation, marketing/IT integration) to functional learning (data sharing).     

The ‘big data’ myth and the pitfalls of ‘thick data’ opportunism: on the need for a different ontology of markets and consumption

ABSTRACT

The twin pillars of big data and data analytics are rapidly transforming the institutional conditions that situate marketing research. In response, many proponents of culturalist paradigms have adopted the vernacular of ‘thick data’ to defend their vulnerable position in the marketing research field. However, thick data proselytising fails to challenge several outmoded ontological assumptions that are manifest in the big data myth and it situates socio-cultural modes of marketing thought in a counterproductive technocratic discourse. In building this argument, I first discuss the relevant historical continuities and discontinuities that have shaped the big data myth and the thick data opportunism. Next, I argue that culturally oriented marketing researchers should promote a different ontological frame— the analytics of marketplace assemblages—to address how big data, or more accurately its socio-technical infrastructure, produces new kinds of emergent and hybrid market structures, modes of social aggregation, consumption practices, and prosumptive capacities.     

Improving survey research using diagrams of data structure

Using diagrams of data structure, or conceptual models, is important in businesses. Survey research often has if-then data structure, but discussion of diagramming survey data structure is rare. This study uses the U.S. Fishing, Hunting and Wildlife-Associated Recreation (FHWAR) survey data and a Taiwan survey in the analysis of benefits of using data structure diagrams in survey research. Examples of data structure diagram use show how diagramming can support consistent and logical data collection, as well as improved data storage and analysis. Analysis also shows how storing if-then (conditional) data in entities/tables allows simple and intuitively meaningful unconditional variable names and can facilitate consideration of conditions that should/can affect analysis. A general conclusion is that the time has come for tourism and business survey researchers to benefit from using diagrams of data structure in planning data accumulation and to benefit from using modern systems in data collection, storage and analysis.     

The perils of working with big data,and a SMALL checklist you can use to recognize them

The use of big data to help explain fluctuations in the broader economy and key business performance indicators is now so commonplace that in some instances it has even begun to rival more traditional measures. Big data sources can very often provide advantages when compared with these more traditional data sources, but with these advantages also come potential pitfalls. We lay out a checklist called SMALL that we have developed in order to help interested parties as they navigate the big data minefield. Based on a set of five questions, the SMALL checklist should help users of big data draw justifiable conclusions and avoid making mistakes in matters of interpretation. To demonstrate, we provide several case studies that demonstrate the subtle nuances of several of these new big data sets and show how the problems they face often closely relate to age-old concerns that more traditional data sources are also forced to tackle.     

Conducting Measurement Validation with Experimental Data: Cautions and Recommendations

In this paper, the authors highlight several problems associated with conducting measurement validation using pooled experimental data. Beginning with a simple two-variable data set, the authors illustrate that pooling data can bias correlation and alpha coefficients, even when the data exhibit homogeneous covariance structures across treatment cells. They then introduce a data set that includes multiple measures for three latent constructs and extend the examination to include the effect of pooling bias on exploratory and confirmatory factor analysis. Three alternative approaches to conducting measurement validation that control for pooling bias are examined.     

CREATING ORDER FORECASTS: POINT‐OF‐SALE OR ORDER HISTORY?

Point‐of‐sale (POS) data, shared by retailers, is often touted as the solution to suppliers' ongoing challenge of accurate order forecasting. However, we find neither empirical evidence of increased order forecast accuracy from the literature, nor consistent use of POS data in suppliers' order forecasting processes. Using a sample containing weekly POS and order data for 10 ready‐to‐eat (RTE) cereal stock‐keeping‐units (SKU's), 7 yogurt SKU's, and 7 canned soup SKU's from 18 retailer distribution centers (DC's) throughout the U.S, our research compares historical POS and order data as order forecasting inputs and finds that POS data does not always outperform order data in terms of order forecast accuracy. While we did find that POS data is a better forecast input in a majority of the forecasts and that on average POS data produces a lower order forecast error, we find that there remain a large number of forecasts where order data is a better predictor than is POS data. Hence, we operationalize this comparison in terms of the frequency and magnitude of order forecast improvement based on POS data. We then hypothesize affecting factors and empirically test these relationships.     

A Primer on Using Behavioral Data for Testing Theories in Advertising Research

Interactions with and between customers in digital, social, and mobile environments are commonly recorded, producing behavioral data that have the potential to advance advertising research. This article provides an accessible guide on how to leverage such data for advertising researchers who may have thus far relied mostly on lab experiment or survey data. Specifically, we suggest potential sources for behavioral data and present a process for analyzing and interpreting behavioral data. Each step of the process is discussed: exploring, understanding and preparing data; specifying and estimating models; and interpreting and presenting the results. Some fundamental issues with using multiple regression to analyze such data are covered, including standardization, outliers, transformations, multicollinearity, and the omitted variable bias. We also discuss issues that are especially problematic with using behavioral data in advertising research, including endogeneity, count data, data with many zeros, and grouped data. More advanced versions of regression that address these issues are surveyed, including instrumental variables, propensity scoring, generalized linear models, and mixed models. General advice for thinking about behavioral data is provided.     

Opportunities for and Pitfalls of Using Big Data in Advertising Research

This editorial introduces the special section on big data. We define big data by examining how it is, or will be, created in advertising environments. We propose a conceptual framework for understanding the different types of digital advertising touch points that create big data, and use the framework for identifying research opportunities. We discuss the types of research questions that big data can inform, including developing and testing theories, identifying insights, and optimizing the delivery of messages. New methods that advertisers will need to use big data are identified. Recommendations are provided for how to think about and approach big data. Using the framework, we identify specific opportunities for advertising researchers to use big data. We also discuss pitfalls in using big data.     

Marketing analytics using anonymized and fragmented tracking data

With the digitization of the retail industry, there is a growing abundance of event-based tracking data describing consumer behavior (e.g., online clickstreams and offline sensors tracking the movement of shoppers). However, stronger data privacy regulations and the growing privacy consciousness of consumers suggest that much of the data may increasingly only be available to retailers in an anonymized and fragmented form that does not identify individual consumers exactly. In response to the relative paucity of research on marketing analytics in retailing using anonymized and fragmented event-based (AFE) tracking data, this paper makes three interrelated contributions. First, we describe the relevance of AFE data in the future of retailing, contrasting it with other forms of aggregate and individual-level data. Second, we propose a methodology for analyzing AFE data, which allows us to approximately recover individual-level heterogeneity and derive meaningful variables from the raw data. Third, we validate the methodology using representative data collected by deploying sensor-enabled shelves in a field experiment within a store. We find that our approach to analyzing AFE data can help uncover interesting patterns of consumer behavior and could be applied across other online and offline retail settings in practice.