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1.
Understanding user experience (UX) becomes more important in a market-driven design paradigm because it helps designers uncover significant factors, such as user’s preference, usage context, product features, as well as their interrelations. Conventional means, such as questionnaire, survey and self-report with predefined questions and prompts, are used to collect information about users’ experience during various UX studies. However, such data is often limited and restricted by initial setups, and they won’t easily allow designers to identify all critical elements such as user profile, context, related product features, etc. Meanwhile, with widely accessible social media, the volume and velocity of customer-generated data are fast-increasing. While it is generally acknowledged that such data contains important elements in understanding and analyzing UX, extracting them to assist product design remains a challenging issue. In this study, how UX data underlying product design can be isolated and restored from customer online reviews is examined. A faceted conceptual model is proposed to elucidate the crucial factors of UX, which serves as an operational mechanism connecting to product design. A methodology of establishing a UX knowledge base from customer online reviews is then proposed to support UX-centered design activities, which consists of three stages, i.e., UX discovery to extract UX data from a single review, UX data integration to group similar data and UX network formalization to build up the causal dependencies among UX groups. Using a case study on smart mobile phone reviews, examples of UX data discovered are demonstrated and both customers and designers concerned key product features and usage situations are exemplified. This study explores the feasibility to discover valuable UX data as well as their relations automatically for product design and business strategic plan by analyzing a large volume of customer online data. 相似文献
2.
Big Data Analytics (BDA) is increasingly becoming a trending practice that generates an enormous amount of data and provides a new opportunity that is helpful in relevant decision-making. The developments in Big Data Analytics provide a new paradigm and solutions for big data sources, storage, and advanced analytics. The BDA provide a nuanced view of big data development, and insights on how it can truly create value for firm and customer. This article presents a comprehensive, well-informed examination, and realistic analysis of deploying big data analytics successfully in companies. It provides an overview of the architecture of BDA including six components, namely: (i) data generation, (ii) data acquisition, (iii) data storage, (iv) advanced data analytics, (v) data visualization, and (vi) decision-making for value-creation. In this paper, seven V's characteristics of BDA namely Volume, Velocity, Variety, Valence, Veracity, Variability, and Value are explored. The various big data analytics tools, techniques and technologies have been described. Furthermore, it presents a methodical analysis for the usage of Big Data Analytics in various applications such as agriculture, healthcare, cyber security, and smart city. This paper also highlights the previous research, challenges, current status, and future directions of big data analytics for various application platforms. This overview highlights three issues, namely (i) concepts, characteristics and processing paradigms of Big Data Analytics; (ii) the state-of-the-art framework for decision-making in BDA for companies to insight value-creation; and (iii) the current challenges of Big Data Analytics as well as possible future directions. 相似文献
3.
传统的新产品开发范式逐渐被客户共创范式所替代,但现有文献缺少对客户共创影响产品创新底层逻辑的探讨。为此,本文假设关系才是中小企业创新资源的真正来源,构建一个有调节的中介效应模型,研究关系的不同维度——感情、人情和信任——对中小企业新产品创新的影响及其客户共创的中介效应,并利用来自长三角和珠三角275家制造型中小企业的样本数据检验假设。研究结果表明,关系的三个维度不仅会直接促进中小企业的新产品绩效,还会通过客户共创的中介作用产生间接影响;调节客户共创与新产品绩效间的关系会受到动态环境的调节,当环境动态性较高时,客户共创对新产品绩效的作用会有所增强。通过对关系影响创新的作用机理更加细粒度的研究,并挖掘了客户共创的中介作用,研究结果有助于解释关系资源对中小企业创新的重要性,并拓展了客户共创理论的研究范畴,也可为中小企业产品创新提供实践指导。 相似文献
4.
《Information processing & management》2022,59(1):102762
Business is based on manufacturing, purchasing, selling a product, and earning or making profits. Social media analytics collect and analyze data from various social networks such as Facebook, Instagram, and Twitter. Social media data analysis can help companies identify consumer desires and preferences, improve customer service and market analytics on social networks, and smarter product development and marketing investments. The business decision-making process is a step-by-step process that enables employees to resolve challenges by weighing evidence, evaluating possible solutions, and selecting a route. In this paper, Big Data-assisted Social Media Analytics for Business (BD-SMAB) Model increases awareness and affects decision-makers in marketing strategies. Companies can use big data analytics in many ways to enhance management. It can evaluate its competitors in real-time and change prices, make deals better than its competitors' sales, analyze competitors' unfavorable feedback and see if they can outperform that competitor. The proposed method examines social media analysis impacts on different areas such as real estate, organizations, and beauty trade fairs. This diversity of these companies shows the effects of social media and how positive decisions can be developed. Take better marketing decisions and develop a strategic approach. As a result, the BD-SMAB method enhance customer satisfaction and experience and develop brand awareness. 相似文献
5.
Surabhi Verma Som Sekhar Bhattacharyya Saurav Kumar 《Information processing & management》2018,54(5):791-806
Research on the adoption of systems for big data analytics has drawn enormous attention in Information Systems research. This study extends big data analytics adoption research by examining the effects of system characteristics on the attitude of managers towards the usage of big data analytics systems. A research model has been proposed in this study based on an extensive review of literature pertaining to the Technology Acceptance Model, with further validation by a survey of 150 big data analytics users. Results of this survey confirm that characteristics of the big data analytics system have significant direct and indirect effects on belief in the benefits of big data analytics systems and perceived usefulness, attitude and adoption. Moreover, there are mediation effects that exist among the system characteristics, benefits of big data analytics systems, perceived usefulness and the attitude towards using big data analytics system. This study expands the existing body of knowledge on the adoption of big data analytics systems, and benefits big data analytics providers and vendors while helping in the formulation of their business models. 相似文献
6.
小微企业对于我国经济和民生的发展具有重要意义，但总体存活率较低，又因其特殊的组织结构和管理方式，导致以大中型企业为研究对象的传统营销理论部分失灵。通过对653家中国新创小微企业的问卷调查数据，本研究从企业最关键的利益相关者──顾客的视角出发，采用结构方程模型和多重线性回归的方法实证检验了一个提升小微企业营销绩效的理论模型。研究发现：利用新创企业资产和实施顾客导向是小微企业增加营销绩效的两种有效途径，且新创企业资产比顾客导向对小微企业营销绩效影响更大。顾客认知合法性感知也对小微企业绩效具有显著积极影响，并在新创企业资产、顾客导向与小微企业营销绩效的关系中起中介作用。竞争强度负向调节了新创企业资产与顾客认知合法性感知的关系。 相似文献
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[目的/意义]实现海量产品评论数据的快速分析,帮助产品设计人员高效地获取用户需求,在新产品设计的决策中提供参考。[方法/过程]在特征提取和情感分析的基础上,构造了包括"词+词性+词干+位置+依存关系"等节点特征的条件随机场模型,按照"产品特征、谁、在何种情境下、遇到了什么问题"4个要素,以描述手机屏幕和电池的负面评论为例,从产品评论中提取用例。[结果/结论]模型评估和实证研究表明,所构造的模型可以有效地从评论文本中识别产品特征、使用主体、使用情景和遇到的问题,从而快速构造用例,获取用户需求。 相似文献
9.
通过虚拟顾客参与平台（Virtual Customer Environments, VCE）来吸引顾客参与创新、利用顾客大数据、创建顾客中心型组织成为企业的重要策略，但VCE的运行机制并未得到理论界的足够重视。本文以VCE功能为主线，将VCE分为信息展示、需求收集和深度互动三个子平台。基于刺激-机体-反应模型，分析每个子平台的运行机制。具体而言，IT环境和市场环境的发展构成VCE的运行基础，企业和顾客之间的交互关系构成VCE的运行动因，企业吸引行为和顾客参与行为构成VCE的运行过程。进一步运用内容分析法，本文检验了中国500强企业网站中VCE子平台的运行，实证研究结果支持理论模型所提出的运行机制。但同时也发现，大多数中国500强企业尚未充分构建需求收集和深度互动这两个子平台，表现为在实际运行中未能有效吸引顾客参与。本研究不仅推动了VCE理论的发展，对顾客参与理论进行了拓展，并且有助于指导企业完善VCE平台的构建，从信息技术的角度塑造顾客导向型组织，提升创新绩效。 相似文献
10.
《Information processing & management》2023,60(2):103218
The rise of online retail platforms has facilitated customers to purchase from the same firm across multiple platform channels. It is challenging for firms to maintain online customer–firm relationships as customers are easily attracted by competitors on different platforms. While previous studies focus on offline-online multichannel shopping, how cross-platform multichannel shopping affects customer–firm relationships is largely neglected. Utilizing longitudinal customer data from an online seller with channels on two major Chinese retail platforms, we found that cross-platform multichannel shopping lengthens, deepens, and broadens online customer–firm relationships. Cross-platform multichannel shopping has a stronger positive effect on the length and depth of online customer–firm relationships as the duration before multichannel shopping increases. After cross-platform multichannel shopping, customers who revert to the original channel weaken their relationship with the firm compared to those who keep using both channels or migrate to another channel. Our empirical results contribute to the literature by examining novel multichannel shopping behavior and its dynamic process. The findings also provide insights into customer relationship management by targeting cross-platform multichannel shopping customers. 相似文献
11.
Big data analytics associated with database searching, mining, and analysis can be seen as an innovative IT capability that can improve firm performance. Even though some leading companies are actively adopting big data analytics to strengthen market competition and to open up new business opportunities, many firms are still in the early stage of the adoption curve due to lack of understanding of and experience with big data. Hence, it is interesting and timely to understand issues relevant to big data adoption. In this study, a research model is proposed to explain the acquisition intention of big data analytics mainly from the theoretical perspectives of data quality management and data usage experience. Our empirical investigation reveals that a firm's intention for big data analytics can be positively affected by its competence in maintaining the quality of corporate data. Moreover, a firm's favorable experience (i.e., benefit perceptions) in utilizing external source data could encourage future acquisition of big data analytics. Surprisingly, a firm's favorable experience (i.e., benefit perceptions) in utilizing internal source data could hamper its adoption intention for big data analytics. 相似文献
12.
作为MSI两次提及的优先研究领域,顾客契合成为当前国际营销科学领域的热点问题之一。研究基于知识图谱理论,利用Citespace软件对Web of Science核心合集上1076篇顾客契合相关文献进行文献计量分析,绘制顾客契合研究文献的共被引知识图谱和共词聚类知识图谱,以探索顾客契合的研究热点和研究趋势。研究发现：顾客契合当前研究热点包括问卷测量开发、顾客契合实证研究和顾客契合在价值创造中的作用;顾客契合的研究趋势包括大数据环境下的顾客契合测量、顾客契合价值的识别与挖掘、在线互动环境中的“游戏化”元素设计研究三个方面。 相似文献
13.
研发项目是涉及顾客需求、产品功能和部件、团队等多知识领域的复杂系统,与大数据技术相关的知识图谱方法可以更加客观全面地展示、分析不同领域间的关联,为此,本文提出新产品开发(NPD)知识图谱,并将其与依赖结构矩阵(DSM)等方法相结合,以识别研发项目中多领域间的相互依赖关系。首先,本文建立依据NPD知识图谱测度顾客需求优先序的模型,并采用DSM和质量功能展开(QFD)方法,建立由“需求-功能”QFD关联推导功能间依赖关系强度的模型。然后,采用“功能-产品”多领域矩阵(MDM)推导部件间的依赖关系强度。最后,对DSM进行聚类,为提高聚类算法的稳定性,采用改进的信息熵,建立了改进的基于信息熵的两阶段DSM聚类模型,算例分析表明,该方法可明显降低类间的协调复杂性并提高算法的稳定性。 相似文献
14.
Over recent years, organizations have started to capitalize on the significant use of Big Data and emerging technologies to analyze, and gain valuable insights linked to, decision-making processes. The process of Competitive Intelligence (CI) includes monitoring competitors with a view to delivering both actionable and meaningful intelligence to organizations. In this regard, the capacity to leverage and unleash the potential of big data tools and techniques is one of various significant components of successfully steering CI and ultimately infusing such valuable knowledge into CI strategies. In this paper, the authors aim to examine Big Data applications in CI processes within organizations by exploring how organizations deal with Big Data analytics, and this study provides a context for developing Big Data frameworks and process models for CI in organizations. Overall, research findings have indicated a preference for a rather centralized informal process as opposed to a clear formal structure for CI; the use of basic tools for queries, as opposed to reliance on dedicated methods such as advanced machine learning; and the existence of multiple challenges that companies currently face regarding the use of big data analytics in building organizational CI. 相似文献
15.
Firms are increasingly relying on business insights obtained by deploying data analytics. Analytics-driven business decisions have thus taken a strategic imperative role for the competitive advantage of a firm to endure. The extent and effectiveness through which business firms can actually derive benefits by deploying big data-based practices requires deep analysis and calls for extensive research. This study extends the big data analytics capability (BDAC) model by examining the mediatory effects of organizational culture (CL) between internal analytical knowledge (KN) and BDAC, as well as the mediating effects of BDAC between CL and firm performance. The findings bring into focus that CL plays the role of complementary mediation between BDAC and KN to positively impact firm performance (FP); BDAC also plays a similar mediatory role between CL and the performance of a firm. 相似文献
16.
《International Journal of Information Management》2016,36(5):748-758
The expansion of big data and the evolution of Internet of Things (IoT) technologies have played an important role in the feasibility of smart city initiatives. Big data offer the potential for cities to obtain valuable insights from a large amount of data collected through various sources, and the IoT allows the integration of sensors, radio-frequency identification, and Bluetooth in the real-world environment using highly networked services. The combination of the IoT and big data is an unexplored research area that has brought new and interesting challenges for achieving the goal of future smart cities. These new challenges focus primarily on problems related to business and technology that enable cities to actualize the vision, principles, and requirements of the applications of smart cities by realizing the main smart environment characteristics. In this paper, we describe the state-of-the-art communication technologies and smart-based applications used within the context of smart cities. The visions of big data analytics to support smart cities are discussed by focusing on how big data can fundamentally change urban populations at different levels. Moreover, a future business model of big data for smart cities is proposed, and the business and technological research challenges are identified. This study can serve as a benchmark for researchers and industries for the future progress and development of smart cities in the context of big data. 相似文献
17.
《International Journal of Information Management》2016,36(2):240-244
Information management during the product lifecycle has received a great deal of attention over the last few years, mainly because firms work in a complex business environment characterized by information overload, high levels of competitiveness and the acceleration of technological change. In this context, Product Lifecycle Management (PLM) software has been evolving rapidly and, today, powerful tools in the market enable high levels of information to be managed. However, commercial PLM software is mostly oriented towards large-sized firms, which poses a big challenge for small and mid-sized enterprises (SMEs). To address this issue, SMEs can develop their own Product Lifecycle Information Management (PLIM) Frameworks for managing data and information throughout the product lifecycle processes. This article presents a successful example of a PLIM Framework: the case of Pladomin’s PLIM Framework. 相似文献
18.
Undoubtedly, the change in consumers’ choices and expectations, stemming from the emerging technology and also significant availability of different products and services, created a highly competitive landscape in various customer service sectors, including the financial industry. Accordingly, the Canadian banking industry has also become highly competitive due to the threats and disruptions caused by not only direct competitors, but also new entrants to the market.The primary objective of this paper is to construct a predictive churn model by utilizing big data, including the structured archival data, integrated with unstructured data from sources such as online web pages, the number of website visits and phone conversation logs, for the first time in the financial industry. It also examines the effect of different aspects of customers’ behavior on churning decisions. The Datameer big data analytics tool on the Hadoop platform and predictive techniques using the SAS business intelligence system were applied to study the client retirement journey path and to create a churn prediction model. By deploying the above systems, we were able to uncover a wealth of data and information associated with over 3 million customers’ records within the retiree segment of the target bank, from 2011 to 2015. 相似文献
19.
Understanding how the application of big data analytics (BDA) generates business value is a persistent challenge in information systems (IS) research. Improving understanding of how BDA realizes business value requires unpacking theories to study the phenomenon. This study unpacks the task-technology fit (TTF) theory toward generating new and improved insights into the business value of BDA. Extant studies on TTF have mainly focused on traditional IT which is different from digital technologies like BDA that are malleable and dynamic. While TTF has primarily focused on how the technology meets task requirements, this study contends that tasks can also be structured to fit the functionality of technology. This study proposes a 2 × 2 matrix framework to explain how BDA and tasks interact. The framework indicates how the reconfigurability of tasks and the editability of BDA impact the fit between tasks and BDA. Future research should explore how the fit between tasks and BDA changes over time. 相似文献
20.
The number of firms that intend to invest in big data analytics has declined and many firms that invested in the use of these tools could not successfully deploy their project to production. In this study, we leverage the valence theory perspective to investigate the role of positive and negative valence factors on the impact of bigness of data on big data analytics usage within firms. The research model is validated empirically from 140 IT managers and data analysts using survey data. The results confirm the impact of bigness of data on both negative valence (i.e., data security concern and task complexity), and positive valence (i.e., data accessibility and data diagnosticity) factors. In addition, findings show that data security concern is not a critical factor in using big data analytics. The results also show that, interestingly, at different levels of data security concern, task complexity, data accessibility, and data diagnosticity, the impact of bigness of data on big data analytics use will be varied. For practitioners, the findings provide important guidelines to increase the extent of using big data analytics by considering both positive and negative valence factors. 相似文献