Revisiting IS research practice in the era of big data

Through building and testing theory, the practice of research animates data for human sense-making about the world. The IS field began in an era when research data was scarce; in today's age of big data, it is now abundant. Yet, IS researchers often enact methodological assumptions developed in a time of data scarcity, and many remain uncertain how to systematically take advantage of new opportunities afforded by big data. How should we adapt our research norms, traditions, and practices to reflect newfound data abundance? How can we leverage the availability of big data to generate cumulative and generalizable knowledge claims that are robust to threats to validity? To date, IS academics have largely welcomed the arrival of big data as an overwhelmingly positive development. A common refrain in the discipline is: more data is great, IS researchers know all about data, and we are a well-positioned discipline to leverage big data in research and teaching. In our opinion, many benefits of big data will be realized only with a thoughtful understanding of the implications of big data availability and, increasingly, a deliberate shift in IS research practices. We advocate for a need to re-visit and extend traditional models that are commonly used to guide much of IS research. Based on our analysis, we propose a research approach that incorporates consideration of big data—and associated implications such as data abundance—into a classic approach to building and testing theory. We close our commentary by discussing the implications of this hybrid approach for the organization, execution, and evaluation of theory-informed research. Our recommendations on how to update one approach to IS research practice may have relevance to all theory-informed researchers who seek to leverage big data.     

Integrating quality into quantity: survey research in the era of mixed methods

As widely recognized during the golden age of survey research thanks to the work of the Columbia school, the use of mixed strategies allows survey research to overcome its limitations by incorporating the advantages of qualitative approaches rather than seeking alternative methods. The need to re-think survey research before embarking on this course impelled the author to undertake a critical analysis of one of the survey’s most important assumptions, proposing a shift from standardization of stimulus to standardization of meanings in order to anchor the requirement of answer comparability on a more solid basis. This proposal for rapprochement with qualitative research is followed by a more detailed section in which the author distinguishes four different types of mixed survey strategies, combining two criteria (time order and function of qualitative procedures). The most significant parts of the constructed typology are then brought together in a model called the multilevel integrated survey approach. This methodological model is concretely illustrated in an empirical study of homophobic prejudice among teenagers. The example shows how in research practice analytical mixed strategies can be creatively combined in the same survey research design, contributing to improvements in data quality and the relevance of research findings.     

大数据时代背景下浅议人才科研能力培养与企业应用

大数据时代呼唤创新性,懂得数据应用技术和研究创造的人才,会更受青睐。如今越来越多的企业在招聘人才时,更多地关注求职应聘者沟通相处能力,创新动手能力甚至管理能力的综合素质,以适应岗位要求和变化的社会环境。创新人才的培养,在高校教育中体现为对学生科研能力的教育要求。对本科生培养大数据意识,重视基本的学习信息采集和信息分析技术,更好的为企业提供运营管理和决策咨询服务。     

A review of techniques for treating missing data in OM survey research

The treatment of missing data has been overlooked by the OM literature, while other fields such as marketing, organizational behavior, economics, statistics and psychometrics have paid more attention to the issue. A review of 103 survey-based articles published in the Journal of Operations Management between 1993 and 2001 shows that listwise deletion, which is often the least accurate technique of dealing with missing data, is heavily utilized by OM researchers. The paper also discusses the research implications of missing data, types of missing data and concludes with recommendations on which techniques should be used under different circumstances in order to improve the treatment of missing data in OM survey research.     

互联网时代对传统企业营销的影响及应对措施研究

文章分析了互联网时代企业营销的转变,以及互联网时代对企业营销的影响,并阐述了互联网时代企业营销转变的出路及应对措施。     

Retrospective studies with survey data: Problems in social science and epidemiology research

Large-scale population surveys are a valuable source of data for epidemiologists and social scientists interested in retrospective studies of health conditions. It may not be apparent that such data sources present many problems, one of which is data reliability. It can be shown statistically that where a relation exists between two variables measured with no or minimum error, this relation vanishes if either or both variables have sizable error components. Thus such data have little utility in assessing simple relations, let alone in making causal inferences. Another problem is that unreliability or measurement error affects the sensitivity and specificity of a measuring instrument in unpredictable ways. Solutions to the problems caused by data unreliability are suggested.     

网上银行业务发展初探

文章就网上银行的特点,我国网上银行业务发展状况、存在问题及解决措施进行了初步探讨。     

Stability and integrity tests in data envelopment analysis

Studies that apply data envelopment analysis often neglect testing the stability of the efficient frontier to data perturbations, and, to a lesser extent, the ability of efficiency scores to correctly discriminate between units on performance (integrity). Our primary motivation is to demonstrate methods that can help reduce the number of managerial decisions based on results that may be unreliable. To this end, we illustrate multiple tests of stability and integrity in an environment of fully units-invariant efficiency measurement. This application of tests of stability and integrity using a slacks-based measure of efficiency is the first in a peer-reviewed journal.     

Mixed strategies for improving data quality: the contribution of qualitative procedures to survey research

This article aims to illustrate how qualitative research strategies contribute to the implementation of the standard survey model by monitoring and improving the quality of collected data and thus the quality of survey findings as well. The methodological cycle for survey data-quality construction using qualitative strategies comprises three sequential stages that alternate with standard procedures: the pilot study, pretesting, and deviant case analysis. If the first two of these stages have enjoyed a certain recognition in the literature, the same cannot be said for deviant case analysis. Deviant case analysis is a research strategy originally proposed by Lazarsfeld and promoted within the Colombia School from the 1940s to the 1960s, unfortunately without subsequently receiving the attention it deserved within the area of survey research in general. It refers to searching outside the collected empirical base for clues concerning anomalous responses revealed by statistical analysis that either deviate from research hypotheses or produce contradictory classificatory results.     

大数据时代下国内企业竞争情报研究分析

21世纪的人类社会是一个信息大爆炸的社会,各种各样的信息数据充斥在每一个角落,我们已经不可逆转地进入了大数据时代。文章基于大数据时代背景,论述了当前国内企业竞争情报研究状况,新的数据环境下企业竞争情报中所凸显的问题,以及对这些问题的分析与展望。     

互联网金融促进经济增长机制研究

本文从互联网金融促进经济增长机制的视角出发,在论证它是一类金融模式,归纳出它具有普惠金融、创新、知识技术密集型、产业集群化发展、支付快捷、交易成本低、信息透明和风险显性伴生等特点的基础上,引入马克思关于经济增长的理论,构建放松的古典—马克思经济增长模型,分析互联网金融通过提升资本保存率,增加资本积累,促进人力资本提升,在技术、产品和服务等领域的多重叠加创新,促进产业融合,降低经济发展成本等多种机制,能够促进社会经济增长.最后根据我国互联网金融发展现状提出几点建议.     

Forecasting the business cycle using survey data

Regular business survey data are published as percentages of firms predicting higher, equal or lower values of some reference variable. Time series of such percentages do not fit production data too well. Univariate models often produce forecasts which are just as accurarate. Still, surveys contain anticipative judgement which, when combined with univariate modeling and proper filtering, may produce a good indicator for business cycle turning points. The way survey data are transformed so as to fit statistics on production seems not to be of much importance. A case study of the Finnish forest industry is offered as an example.     

Forecasting sales in the supply chain: Consumer analytics in the big data era

Forecasts have traditionally served as the basis for planning and executing supply chain activities. Forecasts drive supply chain decisions, and they have become critically important due to increasing customer expectations, shortening lead times, and the need to manage scarce resources. Over the last ten years, advances in technology and data collection systems have resulted in the generation of huge volumes of data on a wide variety of topics and at great speed. This paper reviews the impact that this explosion of data is having on product forecasting and how it is improving it. While much of this review will focus on time series data, we will also explore how such data can be used to obtain insights into consumer behavior, and the impact of such data on organizational forecasting.     

中国当代城市经济理论研究的沿革与发展

城市经济是国民经济的重要领域,对城市经济理论的研究,中国当代学者始终没有停下脚步,尤其是改革开放以来,开展了精彩而深刻的探索,取得了大量的成果。本文旨在梳理、揭示和反映中国当代城市经济理论研究的波澜壮阔的历程。     

Benchmarking robustness of load forecasting models under data integrity attacks

As the internet’s footprint continues to expand, cybersecurity is becoming a major concern for both governments and the private sector. One such cybersecurity issue relates to data integrity attacks. This paper focuses on the power industry, where the forecasting processes rely heavily on the quality of the data. Data integrity attacks are expected to harm the performances of forecasting systems, which will have a major impact on both the financial bottom line of power companies and the resilience of power grids. This paper reveals the effect of data integrity attacks on the accuracy of four representative load forecasting models (multiple linear regression, support vector regression, artificial neural networks, and fuzzy interaction regression). We begin by simulating some data integrity attacks through the random injection of some multipliers that follow a normal or uniform distribution into the load series. Then, the four aforementioned load forecasting models are used to generate one-year-ahead ex post point forecasts in order to provide a comparison of their forecast errors. The results show that the support vector regression model is most robust, followed closely by the multiple linear regression model, while the fuzzy interaction regression model is the least robust of the four. Nevertheless, all four models fail to provide satisfying forecasts when the scale of the data integrity attacks becomes large. This presents a serious challenge to both load forecasters and the broader forecasting community: the generation of accurate forecasts under data integrity attacks. We construct our case study using the publicly-available data from Global Energy Forecasting Competition 2012. At the end, we also offer an overview of potential research topics for future studies.     

浅谈大数据时代如何加强国有企业的财务管理

在数字信息技术的蓬勃发展下,大数据时代已经来临。文章分析了大数据时代给国有企业带来的新机遇和新挑战,在分析了国有企业财务管理亟需加强的关键点后,最后给出了相关建议。     

Problems in the estimation and interpretation of the reliability of survey data

In this paper I discuss several of the difficulties involved in estimating the reliability of survey measurement. Reliability is defined on the basis of classical true-score theory, as the correlational consistency of multiple measures of the same construct, net of true change. This concept is presented within the framework of a theoretical discussion of the sources of error in survey data and the design requirements for separating response variation into components representing such response consistency and measurement errors. Discussion focuses on the potential sources of random and nonrandom errors, including “invalidity” of measurement, the term frequently used to refer to components of method variance. Problems with the estimation of these components are enumerated and discussed with respect to both cross-sectional and panel designs. Empirical examples are given of the estimation of the quantities of interest, which are the basis of a discussion of the interpretational difficulties encountered in reliability estimation. Data are drawn from the ISR's Quality of Life surveys, the National Election Studies and the NORC's General Social Surveys. The general conclusion is that both cross-sectional and panel estimates of measurement reliability are desirable, but for the purposes of isolating the random component of error, panel designs are probably the most advantageous.     

Would you mind completing this survey: Assessing the state of survey research in supply chain management

In supply chain management, survey research is one of the primary methodologies used by researchers to generate empirical data. Critical to its effectiveness is an acceptable response rate. Recently, concerns have been raised regarding perceived falling response rates. Using data taken from 464 survey-based studies, as drawn from five representative journals that publish survey-based supply chain studies, this study assesses whether these concerns are valid. Furthermore, it assesses whether the various tactics recommended for improving response rates have been successful. The findings indicate that (1) responses rates have been falling (with a significant decrease beginning in 2001); (2) it is difficult to evaluate the effectiveness of the various techniques because of unevenness in reporting of techniques used; and, (3) response rates are significantly influenced by factors such as the number of questions, source of survey population, method of survey delivery, specific respondents targeted, and use of prequalification/pre-notification.