When and how consumers are willing to exchange data with retailers: An exploratory segmentation

Consumer data is a crucial resource for retailers. Yet accessing this data increasingly requires consumers to willingly participate in data exchange. This paper draws on social exchange theory and privacy calculus to investigate differences in consumer willingness to exchange data with retailers. Consumers are also profiled on their perceptions of retailer's use and abuse of data, along with the antecedents and outcomes of these perceptions. We employ a cross-sectional quantitative survey and collect data from a sample of 463 US consumers. For statistical analysis, we employ a latent class segmentation and identify six consumer segments which differ in their perceptions of the consumer-retailer data exchange. The key drivers of these segment differences include privacy concerns, technology readiness, and general trust of, and engagement with, retail brands. The segments also differ in their subsequent views towards the use and abuse of their data by retailers, including willingness to exchange data. Hence, when accessing and utilizing consumer data, it is important that brands identify different segments, and adapt their approach accordingly.     

Data-driven business and data privacy: Challenges and measures for product-based companies

To leverage the opportunities provided by the Internet of Things (IoT), product-based companies are exploring new data-driven business opportunities. They may miss these same opportunities, however, owing to data-privacy challenges. These challenges start with the customers of product-based companies, extend to the wider business ecosystem, and continue with the companies themselves. This article identifies 12 data-privacy challenges and introduces 12 measures to address them. These include intuitive recommendations, such as enabling cross-product consent collection, as well as less intuitive measures, such as fostering a can-do attitude in legal units, closing the gap between legal and business initiatives, or implementing a clear process for well-reasoned risk-taking. The following four principles were found to support companies in implementing these measures: (1) letting privacy and data-driven business go hand in hand, (2) putting customers first and turning their privacy preferences into opportunities, (3) aligning risk-management activities with the process of digital service development, and (4) using technology to professionalize legal processes.     

Exploitative and exploratory innovations in emerging economies: The role of realized absorptive capacity and learning intent

This paper investigates the mediating effect of learning intent in transforming local suppliers’ potential absorptive capacity into realized absorptive capacity and its impact on exploitative and exploratory innovation. Using survey data from 155 auto parts manufacturers in Pakistan, we find that local firms’ realized absorptive capacity enables them to develop both exploitative and exploratory innovations. The findings further suggest that local suppliers’ learning intent mediates the relationship between potential and realized absorptive capacity which in turn lead to both types of innovation. In the context of the emerging economy of Pakistan, local suppliers’ absorptive capacity is found to be critically important in spurring exploitative and exploratory innovation, but learning intent enables realized absorptive capacity and thus in conjunction with realized absorptive capacity supports innovation. Consequently, there would be a strong case for policy intervention to assist emerging economy firms in building their absorptive capacity and strengthening their learning intent as a route for promoting innovation and improving their value added position in the global value chains of multinational enterprises.     

Benchmarking sales staffing efficiency in dealerships using extended data envelopment analysis

This study presents an approach to benchmark dealer performance in a business-to-business setting through a rigorous efficiency analysis of sales staff allocation. Using a series of basic and extended data envelopment analysis (DEA) models on data collected from a survey of self-reported financial and statistical information, this research assesses dealer efficiency and compares the efficiency scores to traditional financial benchmarking. Findings support the minimization of outsourcing services that are customer interactive and highly specific to a transaction to differentiate the dealer from its competition. Integrating the results from DEA models, manufacturers obtain a comprehensive view of allocating sales staff to increase dealer efficiency and generating a complementary approach to traditional financial ratio benchmarking in determining best practices to other dealers.     

Big data: Dimensions,evolution, impacts,and challenges

Big data represents a new technology paradigm for data that are generated at high velocity and high volume, and with high variety. Big data is envisioned as a game changer capable of revolutionizing the way businesses operate in many industries. This article introduces an integrated view of big data, traces the evolution of big data over the past 20 years, and discusses data analytics essential for processing various structured and unstructured data. This article illustrates the application of data analytics using merchant review data. The impacts of big data on key business performances are then evaluated. Finally, six technical and managerial challenges are discussed.     

Making use of quantitative content analysis: Insights from academia and business practice

This article features a moderated conversation between the author and five leading researchers and practitioners in the field of quantitative content analysis and, more specifically, computer-aided textual analysis (CATA). The interview subjects discuss the factors contributing to CATA’s rise as a research method as well as the benefits and pitfalls of using CATA. The conversation also covers best-practice advice that companies may employ to increase the accuracy of CATA results. These include using different programs or algorithms for the same problem, known as algorithm error.     

An investigation of skill requirements for business and data analytics positions: A content analysis of job advertisements

Presently, analytics degree programs exhibit a growing trend to meet a strong market demand. To explore the skill sets required for analytics positions, the authors examined a sample of online job postings related to professions such as business analyst (BA), business intelligence analyst (BIA), data analyst (DA), and data scientist (DS) using content analysis. They present a ranked list of relevant skills belonging to specific skills categories for the studied positions. Also, they conducted a pairwise comparison between DA and DS as well as BA and BIA. Overall, the authors observed that decision making, organization, communication, and structured data management are key to all job categories. The analysis shows that technical skills like statistics and programming skills are in most demand for DAs. The analysis is useful for creating clear definitions with respect to required skills for job categories in the business and data analytics domain and for designing course curricula for this domain.     

The law and ethics of big data analytics: A new role for international human rights in the search for global standards

The Economist recently declared that digital information has overtaken oil as the world’s most valuable commodity. Big data technology is inherently global and borderless, yet little international consensus exists over what standards should govern its use. One source of global standards benefitting from considerable international consensus might be used to fill the gap: international human rights law. This article considers the extent to which international human rights law operates as a legal or ethical constraint on global commercial use of big data technologies. By providing clear baseline standards that apply worldwide, human rights can help shape cultural norms—implemented as ethical practices and global policies and procedures—about what businesses should do with their information technologies. In this way, human rights could play a broad and important role in shaping business thinking about the proper handling of this increasingly valuable commodity in the modern global society.     

Content analysis of DBA and PhD dissertations in business

The doctor of business administration (DBA) degree is widely considered as a professional doctorate or professional doctoral degree that is designed to prepare business practitioners. The doctor of philosophy (PhD) in business, by contrast, is thought to prepare research scholars and higher education faculty members. Prior research conducted by the authors comparing the curriculum of DBA and PhD programs, analyzing accreditation and regulation of the degree programs and examining the terminal degrees of business faculty, did not support the theoretical difference between the two degrees. This study consisted of an analysis of 147 DBA versus 151 PhD dissertations. The analysis indicated a significant group mean difference in the number of PhD versus DBA dissertations in institutions with an R1 classification by the Carnegie Commission on Higher Education. However, no significant correlations nor group mean differences were found in methodology choice, sample size, or research type (applied vs. basic), based on the degree type (DBA vs. PhD).     

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.     

中继卫星地面高速数据分发设备设计与性能分析

针对跟踪与数据中继卫星系统（TDRSS）的技术发展趋势及数据分发应用需求,分析了现有基于通用服务器的高速数据分发模式存在的问题及性能瓶颈,在此基础上提出了基于网络处理器的数据分发模式,并分析了其相比于服务器模式的优点。从硬件和软件两方面提出了基于专用网络芯片的高速数据分发设备的设计方案,给出三种高速数据分发方法,通过比较,选用最优方法。性能测试结果表明,基于专用网络处理器的数据分发模式相比于现有服务器数据分发模式性能提高200%.     

民用飞机适航安全性数据追溯性分析与建模

民用飞机适航安全性数据间的追溯性是审定过程中判定系统安全性工作完整性和准确性的重要方法,但随着民用机载产品安全关键特性及机载设备的综合化程度不断提高,相关数据间追溯关系的复杂性也大大增加,给适航安全性审定工作带来较大挑战。为建立安全性数据及其关联性的评价标准及验证方法,从安全性需求确认及验证、分析方法,以及相关运行维护分析等方面总结审定要素和评审要求,提出了一种整合设计数据、安全性数据与运行维护数据的安全性数据追溯模型。通过该模型在型号工作中的具体应用及情况分析,表明其可为我国民机型号安全性评估及审定工作提供理论支撑。     

测量船异常数据辨识与误差抑制

为解决蕴含有复杂变形、船摇周期影响的测量船异常数据检测难题,提出了基于小波分析的异常数据的动态识别与修正方法。基于小波分析的多尺度、局部分析等特性,采用具有紧支撑特性的小波去噪方法,根据数据自适应选取阈值,对噪声进行抑制凸显异常,在实现噪声抑制的同时,较好地保留了局部异常特征,显著提高了异常数据的检出率。测试结果表明,该方法切实可行,效果良好。     

Measuring the financial and social performance of French mutual funds: A data envelopment analysis approach

Socially responsible investors pursue both financial and non-financial goals. In this paper, we attempt to assess the performance of French socially responsible mutual funds (SRMFs). We consider the data envelopment analysis (DEA) approach, which allows us to assign a unique efficiency score while combining financial and social characteristics, using various combinations within different types of risk (total risk, market risk, and downside risk). We report the list of funds found to be DEA efficient using various output-oriented models. We also compare our obtained results with the traditional and modern measures used in the literature (Sharpe, Treynor, and the information ratio). We contribute to the literature by testing the validity of the DEA methodology in the financial context. The findings have important implications for fund selection processes and would be mainly of interest to investors and fund managers who integrate environmental, social, and governance criteria into their investment choices.     

我国企业组织冲突的原因思考及应对策略

经济活动主体的机会主义行为倾向构成了组织冲突发生的必然原因 ;人的有限理性为机会主义的产生奠定了基础 ,因而也进一步增加了组织冲突发生的可能性。企业组织冲突管理始终是企业管理中的一项重要内容 ,企业组织应突破传统的冲突管理观念 ,从管理哲学的高度超越冲突 ,走理性与非理性相结合的冲突管理道路。     

Open trade and skilled and unskilled labor productivity in developing countries: A panel data analysis

Abstract

This paper examines the effect of trade openness on the productivity of skilled and unskilled labor in a group of 36 developing countries using panel data and fixed effect approach. We have developed and utilized an empirical model that readily lends itself to testing the hypothesis posed. Our results support the hypothesis that trade openness has a positive and significant impact on labor productivity for both skilled and unskilled labor in the sample countries. We also observe that the beneficial effect of trade openness is relatively stronger for the skilled labor than the unskilled labor. We conclude that contrary to the claim made by Mayda and Rodrik (2001 Mayda, A. M. and Rodrik, D. 2001. “Why are some people (and countries) more protectionist than others? A cross country analysis”. Mimeo: Harvard University.  [Google Scholar]), skilled workers in developing countries may oppose protectionism. When adjusting for the purchasing power parity, the impact of trade openness on labor productivity, although positive and significant, is not as pronounced as it is for other definitions of openness.     

Returnee faculty members,network position and diversification strategy: an analysis of business schools in China

This paper builds on resource-based and network theories to pose an integrative framework for understanding diversification strategy of MBA curricula in Chinese business schools. Using a data set collected from 41 schools offering MBA degrees, we find that returnee faculty members exhibit a curvilinear effect on curricular diversification. We also find that network centrality moderates the relationship between returnee faculty members and diversification in those educational institutions with insufficient returnee faculty members who can nonetheless achieve synergies by utilizing their network connections. These results not only offer explanations on how organizations in developing countries can overcome limited internal resources disadvantages but also provide specific insights into how business schools in China can offset the challenges of improving their education programmes.     

Social media user behavior analysis applied to the fashion and apparel industry in the big data era

With the rapid development of the Internet and new media, the enormous data from social media and other public platforms have attracted increasing attention to user behavior research. Fashion is a hot topic for both the general public and the social economy, so there is great potential for exploring and manipulating social media data for the development of the fashion industry in the current era. This paper aims to explore the current status of social media user behavior analysis applied to the fashion and apparel industry for advanced product development, branding strategy planning and the resolution of other decision-making problems in the big data context. First, articles were retrieved from the database "Web of Science". With the assistance of experts, 201 articles were determined for further study. Next, the evolution of hot topics within the domain was visualized and discussed through bibliometric analysis. Then, the application of social media data in the fashion domain was investigated, and common data mining methods were discussed. Finally, the paper summarized the development status of social media user behavior analysis (SMUBA) applied to the fashion and apparel industry and put forward the future prospect under the framework of mass customization in the big data era.     

Information systems security job advertisement analysis: Skills review and implications for information systems curriculum

The authors' focus was the assessment of skill requirements for information systems security positions to understand expectations for security jobs and to highlight issues relevant to curriculum management. The analysis of 798 job advertisements involved the exploration of domain-related and soft skills as well as degree and certification requirements. Domain-related skills comprised the largest part of the results (16 of the top 20 skills) with the top three related to networks, standards, and policies. The most often found soft skill related to teams or teamwork. Implications for information systems curriculum are discussed, and suggestions for future research are provided.