Developing smart service concepts: morphological analysis using a Novelty-Quality map

Among many techniques for generating new service ideas, morphological analysis has been used due to the advantage of decomposing the system into dimensions and shapes and bringing creative results in the process of recombining them. However, with the rise of smart service systems, the determination of dimensions and shapes has become a critical problem, particularly as both their structure and components become complex. This research focuses on a data-driven approach by incorporating mobile app service documents to increase objectivity and diversity in the construction of a morphology matrix. To this end, firstly, the novelty-quality map is developed to identify innovative data based on quantitative indicators. Secondly, morphological analysis is employed along with experts’ judgment in order to generate new smart service concepts. Finally, the feasibility and effectiveness of the proposed approach are shown based on a comparative analysis with conventional approaches and real services through a case study of smart home.     

Geographies of Algorithmic Violence: Redlining the Smart City

City governments are embracing data-driven and algorithmic planning to tackle urban problems. Data-driven analytics have an unprecedented capacity to call urban futures into being. At the same time, they can depoliticize planning decisions. I argue that this shift calls urban studies scholars to investigate geographies of algorithmic violence—a repetitive and standardized form of violence that contributes to the racialization of space and spatialization of poverty. This article examines this broader phenomenon through the case of a proprietary market value assessment that is being used to guide development in cities across the United States. The assessment employs an algorithm that helps city officials make critical decisions about which neighborhoods to target for investment, disinvestment and public service upgrades or disconnections. I argue that the racial, infrastructural, and epistemological violence associated with this evaluation can potentially lead to a new kind of municipal redlining. The article brings insights from critical race theory into conversation with critical scholarship on algorithms by analyzing how algorithmic violence works through data-driven planning technologies to depoliticize and leverage power while further entrenching racism and inequality.     

中国金融科技:面临障碍与发展路径

金融科技是金融和科技的深度融合,随着互联网技术和大数据技术的快速发展,金融科技以颠覆式的速度和方式对传统金融进行了解构和重塑,改变了社会经济增长方式。金融科技通过加速信用资本化进程、提高金融风控能力、降低金融成本等提升了金融服务实体经济的效率,并在我国取得了快速发展,但仍面临数据隐私、数据确权、技术垄断、数据垄断、信息不对称等问题。美国、英国、新加坡等国金融科技的发展对我国具有借鉴意义,可通过加强金融消费者保护、鼓励金融科技创新、加强金融科技监管等措施促进我国金融科技的健康发展。     

BIG data – BIG gains? Understanding the link between big data analytics and innovation

ABSTRACT

This paper analyzes the relationship between firms' use of big data analytics and their innovative performance in terms of product innovations. Since big data technologies provide new data information practices, they create novel decision-making possibilities, which are widely believed to support firms' innovation process. Applying German firm-level data within a knowledge production function framework we find suggestive evidence that big data analytics is a relevant determinant for the likelihood of a firm becoming a product innovator as well as for the market success of product innovations. These results hold for the manufacturing as well as for the service sector but are contingent on firms' investment in IT-specific skills. Overall, the results support the view that big data analytics have the potential to enable innovation.     

论归纳法和依照数据处理的语言学习方法

阐述了归纳法和演绎法在外语学习中所具有的各自优势,并提出依照数据处理法学习外语,可以将两者的优势有机地结合在一起。该学习法中所用的外语语料库比起传统的英文教科书上的语言文字材料更加地道真实,因此,大多数外语学习者可能会对学习外语更加感兴趣,它将有助于外语学习者更好地应用所学的语言。     

Aspects of statistical computing, past, present and future

The various stages in the history of statistical computing are illustrated by personal experiences. The stages include batch processing, interactive working, and the consultative mode. Statistical aspects associated with one or more of these stages include the development of general algorithms, procedures for model checking, and data-driven non-parametric modelling.     

基于数据驱动的企业生产成本精细化管理研究

为了降低企业生产成本,提高其在市场中的竞争力,论文对基于数据驱动的企业生产成本精细化管理进行了研究。通过分析精细化管理的研究价值与研究过程,论文提出了基于数据驱动的管理方式,包括对企业生产成本数据进行有效分析,规划企业生产管理部门的成本管理职责,重构企业生产成本管理流程,从而实现对企业生产成本的精细化管理。     

大数据技术对市场监管的启发—数据驱动的质量风险评估

质量信息难以获取一直是政府质量治理面临的重大挑战.互联网及大数据技术的发展使多元异构数据的采集、处理及动态分析成为可能.聚焦基于大数据的质量风险评估,提出了数据驱动的质量风险评估思路,系统性地识别供应风险、经营风险、准入风险、诚信风险和技术风险五种不同模式的风险来源,通过散落在互联网中的数据构建了不同模式风险来源的表征指标,最终设计了质量风险评估系统框架,并以产品缺陷风险预警为例验证了框架的实用性和有效性.研究结果为大数据技术在质量风险评估中的应用提供了有益的启示.     

Sustainable development in aviation logistics: Successful drivers and business strategies

Firms looking to implement sustainable development confront the problem of choosing among myriad options. To help them overcome it, this study develops two-stage data-driven analytics aimed at establishing which alternatives for sustainable development are most likely to succeed, based on careful scrutiny of sustainable-aviation cases. Specifically, it first ascertains the factors that are most critical to aviation logistics in general and then goes on to identify the vital drivers of successful sustainable development in the same field. The analytical results of the proposed models reveal that sustainable initiatives (ecological conservation and environmental R&D), weight, conversion, and fuel efficiency are successful drivers for organizations to achieve environmental sustainability in aviation logistics. Furthermore, natural-resource-based view theory is utilized to develop business strategies that involve eco-friendly initiatives, product stewardship, and clean technologies.     

Improved Understanding of Tourists' Needs

ABSTRACT

Data-driven segmentation has become standard practice in strategic marketing. Typically, however, respondents are grouped only once, implicitly assuming deterministic nature of the segmentation methods applied. Once the segments are derived, background variables are used to test the significance of the difference between clusters indicating external validity of the market segments. High external validity implies a high level of trustworthiness of the solution and thus manageri-ally useful market segments to choose from. However, single runs of explorative analysis remain only a weak basis for good long-term managerial decisions. In this study a different approach is suggested to im-prove the quality of the market structure insight for decision-making: two data-driven segmentation solutions are constructed independently. Association between them is used as an additional internal validity indicator.

Benefit and behavioural segmentation bases are used to illustrate the concept: surfers provided information regarding the importance of aspects of proposed surf destinations and destinations they had previously visited. Segments resulting from both bases are profiled: they notably differ in background information. Significant association between these solutions supports the validity and managerial usefulness for target segment choice for the following targeted marketing action.

Using this procedure results in the identification of stable consumer groups, which in turn leads to an improved understanding of customer segments; and thus enables the industry to improve the quality by customizing the product.