Data analytics and firm performance: An empirical study in an online B2C platform

Data analytics has become an increasingly eye-catching practice in both the academic and the business communities. The importance of data analytics has also prompted growing literature to focus on the design of data analytics. However, the boundary conditions for data analytics in creating value have been largely overlooked in the literature. The objective of this article therefore is to examine the business value of data analytics usage and explore how such value differs in different market conditions. We rely on an online B2C platform as our empirical setting and obtain several important insights. First, both demand-side and supply-side data analytics usage has a positive effect on the performance of merchants. Second, when merchants’ product variety is high, the influence of usage toward demand-side data on performance is strengthened, whereas such impact is weakened for supply-side data analytics. Third, when competitive intensity is high, the performance implication of demand-side data analytics usage is strengthened, whereas such impact is not strengthened for supply-side data analytics. This study contributes by advancing the overall understanding of business value of data analytics.     

Measuring firm performance using financial ratios: A decision tree approach

Determining the firm performance using a set of financial measures/ratios has been an interesting and challenging problem for many researchers and practitioners. Identification of factors (i.e., financial measures/ratios) that can accurately predict the firm performance is of great interest to any decision maker. In this study, we employed a two-step analysis methodology: first, using exploratory factor analysis (EFA) we identified (and validated) underlying dimensions of the financial ratios, followed by using predictive modeling methods to discover the potential relationships between the firm performance and financial ratios. Four popular decision tree algorithms (CHAID, C5.0, QUEST and C&RT) were used to investigate the impact of financial ratios on firm performance. After developing prediction models, information fusion-based sensitivity analyses were performed to measure the relative importance of independent variables. The results showed the CHAID and C5.0 decision tree algorithms produced the best prediction accuracy. Sensitivity analysis results indicated that Earnings Before Tax-to-Equity Ratio and Net Profit Margin are the two most important variables.     

Analytics Capabilities and the Decision to Invest in Analytics

The increasing use of data-driven decision making and big data is leading organizations to invest in analytics software and services. However, little is known about the type of analytics capabilities within IT that are required and whether there is a common progression or development model of analytics capabilities. Also unknown is how the level of analytics capabilities and other factors influence a firm’s decision to invest in analytics. The purpose of this research is to explore the relationships between levels of distinct analytics capabilities and to understand how they and other factors influence the analytics investment decision. The findings suggest that there is a distinct progression in the development of analytics capabilities, and that firm size is associated with increased capability. The results suggest that firms more likely to invest in analytics have higher current levels of specific analytics capabilities, are larger, and are located in less-competitive industries.     

Quality-optimized predictive analytics

On-line statistical and machine learning analytic tasks over large-scale contextual data streams coming from e.g., wireless sensor networks, Internet of Things environments, have gained high popularity nowadays due to their significance in knowledge extraction, regression and classification tasks, and, more generally, in making sense from large-scale streaming data. The quality of the received contextual information, however, impacts predictive analytics tasks especially when dealing with uncertain data, outliers data, and data containing missing values. Low quality of received contextual data significantly spoils the progressive inference and on-line statistical reasoning tasks, thus, bias is introduced in the induced knowledge, e.g., classification and decision making. To alleviate such situation, which is not so rare in real time contextual information processing systems, we propose a progressive time-optimized data quality-aware mechanism, which attempts to deliver contextual information of high quality to predictive analytics engines by progressively introducing a certain controlled delay. Such a mechanism progressively delivers high quality data as much as possible, thus eliminating possible biases in knowledge extraction and predictive analysis tasks. We propose an analytical model for this mechanism and show the benefits stem from this approach through comprehensive experimental evaluation and comparative assessment with quality-unaware methods over real sensory multivariate contextual data.     

Improved similarity assessment and spectral clustering for unsupervised linking of data extracted from bridge inspection reports

Textual bridge inspection reports are important data sources for supporting data-driven bridge deterioration prediction and maintenance decision making. Information extraction methods are available to extract data/information from these reports to support data-driven analytics. However, directly using the extracted data/information in data analytics is still challenging because, even within the same report, there exist multiple data records that describe the same entity, which increases the dimensionality of the data and adversely affects the performance of the analytics. The first step to address this problem is to link the multiple records that describe the same entity and same type of instances (e.g., all cracks on a specific bridge deck), so that they can be subsequently fused into a single unified representation for dimensionality reduction without information loss. To address this need, this paper proposes a spectral clustering-based method for unsupervised data linking. The method includes: (1) a concept similarity assessment method, which allows for assessing concept similarity even when corpus or semantic information is not available for the application at hand; (2) a record similarity assessment method, which captures and uses similarity assessment dependencies to reduce the number of falsely-linked records; and (3) an improved spectral clustering method, which uses iterative bi-partitioning to better link records in an unsupervised way and to address the transitive closure problem. The proposed data linking method was evaluated in linking records extracted from ten bridge inspection reports. It achieved an average precision, recall, and F-1 measure of 96.2%, 88.3%, and 92.1%, respectively.     

A Lack of Insight: An Experimental Analysis of R&D Managers’ Decision Making in Innovation Portfolio Management

This study analyses the decision to exploit an innovation project and investigates differences in individuals’ evaluations of project attributes in the context of innovation project portfolio management. A conjoint field experiment was used to collect data on exploitation decisions made by 126 research and development (R&D) managers to test how managers evaluate specific project attributes in the context of innovation project portfolio management. I analyse the relative power and popularity of profitability, strategy, uncertainty and social dimensions of the portfolio while R&D managers exploit an innovation project. Moreover, using social judgement theory, I analyse actual exploitation processes (i.e., the innovation attributes an R&D manager considers while he or she is making an exploitation decision) and self‐reported decision‐making attributes (i.e., managers’ self‐reported data). The data underline that R&D managers value specific project attributes more and others less, and therefore find disparities in innovation project portfolio decision making. Based on this study's results, decision makers are better able to reflect and understand the influence of specific project attributes. Therefore, they should investigate established decision‐making processes which can help them to improve portfolio performance.     

Applying social learning analytics to message boards in online distance learning: A case study

Social learning analytics introduces tools and methods that help improving the learning process by providing useful information about the actors and their activity in the learning system. This study examines the relation between SNA parameters and student outcomes, between network parameters and global course performance, and it shows how visualizations of social learning analytics can help observing the visible and invisible interactions occurring in online distance education.The findings from our empirical study show that future research should further investigate whether there are conditions under which social network parameters are reliable predictors of academic performance, but also advises against relying exclusively in social network parameters for predictive purposes. The findings also show that data visualization is a useful tool for social learning analytics, and how it may provide additional information about actors and their behaviors for decision making in online distance learning.     

The role of GPS-enabled information in transforming operational decision making: an exploratory study

Although the impact of ICT-enabled information on firm performance has been well documented in the business value of IT literature, our understanding of how Global Positioning System (GPS) adoption can transform operational decision making and foster differential firm performance is limited. In response, we conduct an exploratory comparative case study of three transport firms that have implemented the same GPS during the same year in their operations. Our results highlight that increased use of GPS-enabled information can enhance information quality and make operational decision making more fact-based and collaborative. We also find that such transformations in operational decision making, driven by increased use of GPS-enabled information, can foster differential performance impacts. However, we warn scholars and practitioners that a firm’s information management capability (in terms of availability of quality information in decision making, software tools for connectivity and access to information, IT systems integration post-GPS adoption and adaptability of the infrastructure to emerging business needs) and organizational factors (such as top management support, project management of GPS implementation, financial support, end-user involvement, rewarding, training and employee resistance) can facilitate (or inhibit) effective use of GPS-enabled information in operational decision making, and thus moderate differential performance benefits of GPS adoption.     

Impact of information quality and decision-maker quality on decision quality: a theoretical model and simulation analysis

The impact of information technology (IT) on firm performance is widely studied but little understood. A common perception is that IT improves the quality of information, which, in turn, improves decision quality and performance. Several studies of IT-performance relationship have used managers' perceived as opposed to actual performance. We investigate the impact of information quality and decision-maker quality on actual decision quality using a theoretical and a simulation model. We use accuracy as the measure of quality. Our analysis shows that, depending on the decision-maker quality, decision quality may improve or degrade when information quality improves. The decision quality improves with higher information quality for a decision-maker that has knowledge about the relationships among problem variables. However, the decision quality of a decision-maker that doesn't know these relationships may degrade with higher information quality. Simultaneous improvement in information quality and decision-maker quality results in higher decision quality. The simulation model, which relaxes some of the assumptions made by the theoretical model, yields similar results. We explain how our results supplement the results of prior studies of IT-performance relationship. Our results underscore the need for including decision-maker quality in the investigation of the IT-performance relationship and the importance of developing quality decision support tools.