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.     

广告监测数据

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广告监测数据

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Artificial Intelligence and Marketing: Pitfalls and Opportunities

This article discusses the pitfalls and opportunities of AI in marketing through the lenses of knowledge creation and knowledge transfer. First, we discuss the notion of “higher-order learning” that distinguishes AI applications from traditional modeling approaches, and while focusing on recent advances in deep neural networks, we cover its underlying methodologies (multilayer perceptron, convolutional, and recurrent neural networks) and learning paradigms (supervised, unsupervised, and reinforcement learning). Second, we discuss the technological pitfalls and dangers marketing managers need to be aware of when implementing AI in their organizations, including the concepts of badly defined objective functions, unsafe or unrealistic learning environments, biased AI, explainable AI, and controllable AI. Third, AI will have a deep impact on predictive tasks that can be automated and require little explainability, we predict that AI will fall short of its promises in many marketing domains if we do not solve the challenges of tacit knowledge transfer between AI models and marketing organizations.     

大数据时代电子数据审计问题研究

近些年大数据得到快速的发展,由于大数据具备对海量数据的收集和分析能力,使得大数据能够在各行各业都被相应的运用,而在审计工作上也得益于大数据提升审计效应。会计信息化的到来,加剧了电子数据审计方式的变革,通过大数据技术可以为审计工作者提供更具价值的决策信息,提高审计工作的效率和全面准确性。因此,大数据也受到了越来越多关注。本文对大数据环境下的电子数据审计研究和应用进行分析,并针对审计研究事项提出合理性完善对策。     

大数据经济学研究现状与发展展望

"大数据"作为一座黄金矿,不仅仅作为工具和助推器推动着信息经济的快速发展,也是一种强有力的生产力。本文以大数据对社会经济的推动作用为出发点,提出了大数据经济学的主要研究对象,包括大数据对决策的影响,对人类生活重心的影响,对市场结构和商业模式的影响等。     

大数据人才分类培养研究

随着大数据技术在各行各业的应用不断加深,大数据人才短缺问题变得日益严峻.现有的大数据人才多是按照岗位类型或者人才类型来进行划分的,本文则从大数据人才工作的内容出发将大数据人才分为数据驱动型人才和数据操作型人才,并对两类人才的内涵进行界定.数据驱动型人才是指在传统岗位上利用数据来驱动业务;数据操作型人才主要是生产大数据产...     

Methodological Guidelines for Advertising Research

In this article, highly experienced advertising academics and advertising research consultants John R. Rossiter and Larry Percy present and discuss what they believe to be the seven most important methodological guidelines that need to be implemented to improve the practice of advertising research. Their focus is on methodology, defined as first choosing a suitable theoretical framework to guide the research study and then identifying the advertising responses that need to be studied. Measurement of those responses is covered elsewhere in this special issue in the article by Bergkvist and Langner. Most of the frameworks are derived from the authors' own published work, although other frameworks are noted where appropriate.     

大数据时代高校创新创业教育研究

创新创业是国家经济发展的重要引擎,当前大众创业、万众创新已成为时代鲜明的主题,大学生创新创业教育受到广泛关注。本文基于大数据时代背景下,结合当前社会发展需要,通过分析当前高校创新创业现状及不足,构建了高校创新创业教育模型,并结合大数据技术的相关应用,实现高校、社会和企业的关联耦合,以有效促进我国高校创新创业教育的发展。     

Taming Uncertainty in Big Data

While the classic definition of Big Data included the dimensions volume, velocity, and variety, a fourth dimension, veracity, has recently come to the attention of researchers and practitioners. The increasing amount of user-generated data associated with the rise of social media emphasizes the need for methods to deal with the uncertainty inherent to these data sources. In this paper we address one aspect of uncertainty by developing a new methodology to establish the reliability of user-generated data based upon causal links with recurring patterns. We associate a large data set of geo-tagged Twitter messages in San Francisco with points of interest, such as bars, restaurants, or museums, within the city. This model is validated by causal relationships between a point of interest and the amount of messages in its vicinity. We subsequently analyze the behavior of these messages over time using a jackknifing procedure to identify categories of points of interest that exhibit consistent patterns over time. Ultimately, we condense this analysis into an indicator that gives evidence on the certainty of a data set based on these causal relationships and recurring patterns in temporal and spatial dimensions.     

中国广告业的数据问题

数据，是被人用来解释状况与帮助决策的。中国的广告业的数据，发展的时间比较短，中国的社会又进步的飞快，本身就比较需要提升；又加上媒体生态的急速数据化，以及新媒体的风起云涌，更是广告业应特别注意的问题。本文将单一数据（如千人成本）的详细讨论，留给学术界以及调研公司，而在哲学的层面上来看看中国广告业数据的问题：首先，是有没有数据；其次，是数据合不合适；再来，是数据的公平、公正、与公开（可信度）问题；还有，就是数据的处理、解释与比较问题。最后，我们探讨数据的垄断问题。[第一段]     

Meta-Analysis in Advertising Research

Meta-analyses have become increasingly popular in many fields of the social sciences, including advertising research. The results of meta-analyses attract substantial interest from both scholars and practitioners, as indicated by high citation numbers and widespread dissemination in the media. By summarizing results drawn from a set of studies concerning a specific topic, and by discovering and explaining consistencies and inconsistencies within those results, meta-analysis is an essential step in the process of knowledge accumulation, theory building, and theory testing in science. Many developments in the last years have contributed to the advancement of the method, but they have appeared in disparate literatures. Furthermore, each discipline applies the method in a particular way that corresponds to the major research traditions in a field. However, no attempt to introduce the method to advertising researchers is available. The purpose of this article is therefore to provide a comprehensive overview of the meta-analysis method to advertising researchers that describes the procedures commonly applied in advertising research, that provides a better understanding of the method and evaluation criteria for judging the quality of meta-analyses, and that serves as a starting point for advertising researchers who are interested in conducting a meta-analysis.     

Dilemmas in Advertising Research

Abstract

Much advertising research is likely to seem trivial to advertising practitioners and academicians. This is because of two central dilemmas in advertising research. First is the dilemma of purpose: advertising research is too often undertaken for its own sake and thus seems to have a trivial purpose. Second is the dilemma of method: advertising research is often perceived as trivial because it is based on faulty or ill-conceived research procedures. There is much opportunity, particularly for academicians, to pursue non-trivial advertising research. This opportunity originates in the practice of advertising but is not trivial because of it. Specific examples of non-trivial research opportunities in copy research and media research are described and discussed.     

Qualitative Research in Advertising

In spite of the rise of big data and the ease with which online experiments and surveys may be conducted, there is more need than ever for qualitative advertising research. This review considers both the methods and findings of such research. It focuses on the role of qualitative analyses in revealing how ads are “read” by consumers. Methodological approaches considered include observation, depth interviews, projective methods, focus groups, netnography, and videography. Theoretical approaches reviewed include semiotics, reader response, and co-optation theory. Because cultural analyses are a strength of qualitative research, global and cross-cultural advertising research is also examined. The article concludes with an evaluation of the ways in which qualitative advertising research can be combined with data analytics to produce richer and more complete understandings of consumer behavior in response to advertising.     

大数据时代食品监管模式创新性研究

以大数据为时代背景,通过研究食品监管制度,创造性地提出企业定制监管模式。大数据是一种对海量数据处理分析的技术手段,将大数据的手段运用到食品监管中,建立企业数据库、数据共享平台和数据公开平台,对食品企业数据进行收集与挖掘,为企业定制个性化的监管模式,有针对性地进行监管,优化监管资源的配置,提升监管效能。