A Review of Big Data Research in Accounting

The impending fourth industrial revolution has enhanced the role of big data analytics in today’s business practice. Consequently, many now consider big data as the most strategic resource in business to the extent that organizations that fail to utilize it may become competitively disadvantaged. Following these developments, questions have been raised about the future of the accounting discipline, especially in terms of how it can continue to add value to organizations. While some scholars have attempted to address this question, it remains an abstract concept requiring further investigation. Therefore, this study conducts a systematic literature review to determine the status of accounting research on big data analytics and provides avenues for further studies. By conducting co-occurrence network analysis on 52 peer-reviewed articles published from 2010 to 2020, three broad themes emerged, entailing big data implications for accounting practice, education, and research design. A further examination of the themes revealed few empirical studies on the phenomenon, as conceptual research dominates the field. Although external audit implications of big data are widely discussed, other accounting domains (e.g., managerial accounting and taxation) are underexplored. Therefore, future studies may focus on the implications of big data on variables such as performance measurement, information governance, tax behavior, curriculum design, and pedagogy.     

Simplicity is not key: Understanding firm-generated social media images and consumer liking

Social media platforms are becoming increasingly important marketing channels, and recently these channels are becoming dominated by content that is not textual, but visual in nature. In this paper, we explore the relationship between the visual complexity of firm-generated imagery (FGI) and consumer liking on social media. We use previously validated image mining methods, to automatically extract interpretable visual complexity measures from images. We construct a set of six interpretable measures that are categorized as either (1) feature complexity measures (i.e., unstructured pixel-level variation; color, luminance, and edges) or (2) design complexity measures (i.e., structured design-level variation; number of objects, irregularity of object arrangement, and asymmetry of object arrangement). These measures and their interpretability are validated using a human subject experiment. Subsequently, we relate these visual complexity measures to the number of likes. The results show an inverted u-shape between feature complexity and consumer liking and a regular u-shape relationship between design complexity and consumer liking. In addition, we demonstrate that using the six individual measures that constitute feature- and design complexity provides a more nuanced view of the relationship between the unique aspects of visual complexity and consumer liking of FGI on social media than observed in previous studies that used a more aggregated measure. Overall, the automated framework presented in this paper opens up a wide range of possibilities for studying the role of visual complexity in online content.     

Individual adoption of HR analytics: a fine grained view of the early stages leading to adoption

Abstract

High performing organizations are using analytics for evidence-based decision-making. However, the human resource (HR) function in many organizations has been slow to adopt this innovation. This study applies innovation theory, informed by the Theory of Planned Behavior (TPB), to examine the individual’s decision to adopt HR Analytics in an effort to identify why the adoption rate is lagging. We examined early stages of the individual decision process beginning from Stage 1 (knowledge) and leading to Stage 3, (the decision) to adopt or not to adopt the innovation. We found several points in the process that can act as barriers or facilitators. Organizations and champions of this innovation wishing to facilitate HR analytics adoption can take action to remove as many of these barriers to the individual’s decision as possible. Further research should focus on the best way to remove these barriers.     

The role of machine learning analytics and metrics in retailing research

This research presents the use of machine learning analytics and metrics in the retailing context. We first discuss what is machine learning and explain the field’s origins. We then demonstrate the strengths of machine learning methods using an online retailing dataset, noting key areas of divergence from the traditional explanatory approach to data analysis. We then provide a review of the current state of machine learning in top-level retailing and marketing research, integrating ideas for future research and showcasing potential applications for practitioners. We propose that the explanatory and machine learning approaches need not be mutually exclusive. Particularly, we discuss four key areas in the general scientific research process that can benefit from machine learning: data exploration/theory building, variable creation, estimation, and predicting an outcome metric. Due to the customer-facing nature of retailing, we anticipate several challenges researchers and practitioners might face in the adoption and implementation of machine learning, such as ethical prediction and customer privacy issues. Overall, our belief is that machine learning can enhance customer experience and, accordingly, we advance opportunities for future research.     

Google Analytics for measuring website performance

Performance measurement of tourism websites is becoming a critical issue for effective online marketing. The aim of this article is to analyse the effectiveness of entries (visit behaviour and length of sessions) depending on their traffic source: direct visit, in-link entries (for instance, en.wikipedia.org), and search engine visits (for example, Google). For this purpose, time series analysis of Google Analytics data is made use of. This method could be interesting for any tourism website optimizer.     

Managing customer knowledge through the use of big data analytics in tourism research

The use of big data is growing in relevance and importance in tourism management research. Companies operating in this industry are exploiting big data analytics and developing systems to manage customer knowledge and provide the best service in the right place at the right time. This paper aims to provide a systematic literature review to present issues associated with the use of big data in tourism and identify future research directions on the topic. To achieve this aim, this paper develops a citation network analysis methodology to drive the content analysis and explore the content of 109 selected papers. The findings of this review highlight that although there is an increasing number of contributions on the topic, there are yet some issues that require to be further developed. In particular, the paper identifies research gaps and consequent research questions that represent an agenda for both researchers and practitioners.     

组织视阈下大数据分析能力的整合分析框架构建与未来展望

面对数字化情境,企业通过多元化开发与策略性利用大数据分析能力获得高绩效和竞争优势。大数据分析能力可用于解释企业如何依托数据发挥经济效应,也可为解释企业差异化绩效提供新证据。通过阐述大数据分析能力的概念内涵与测量方法,分析其前置动因和作用机制,构建大数据分析能力整合分析模型,并提出未来潜在研究方向：从资源、能力和整合3种视角丰富大数据分析能力的概念内涵和测量工具;结合社会整合理论,挖掘大数据分析能力的差异化作用机制;从组织支持和团队领导两方面探索大数据分析能力的形成机制;关注大数据分析能力发生的重要情境和边界条件。结论有助于全面认识大数据分析能力的形成过程和作用机制,丰富其在组织管理领域的研究。     

Forecasting third-party mobile payments with implications for customer flow prediction

Forecasting customer flow is key for retailers in making daily operational decisions, but small retailers often lack the resources to obtain such forecasts. Rather than forecasting stores’ total customer flows, this research utilizes emerging third-party mobile payment data to provide participating stores with a value-added service by forecasting their share of daily customer flows. These customer transactions using mobile payments can then be utilized further to derive retailers’ total customer flows indirectly, thereby overcoming the constraints that small retailers face. We propose a third-party mobile-payment-platform centered daily mobile payments forecasting solution based on an extension of the newly-developed Gradient Boosting Regression Tree (GBRT) method which can generate multi-step forecasts for many stores concurrently. Using empirical forecasting experiments with thousands of time series, we show that GBRT, together with a strategy for multi-period-ahead forecasting, provides more accurate forecasts than established benchmarks. Pooling data from the platform across stores leads to benefits relative to analyzing the data individually, thus demonstrating the value of this machine learning application.     

Leveraging the synergies between design science and behavioral science research methods

For over two decades, information systems researchers have grappled with defining what constitutes good design science research. With too many older papers simply documenting the development of systems without a clear message of the contribution to science, design science fell out of favor with the information systems discipline. With the emergence of intelligent systems and the re-shaping of knowledge work, substantial effort has recently focused on articulating what constitutes a design science research contribution. In recent years, however, the discussion on the role of behavioral theory and behavioral research in complementing design science research has faded away. In this paper, we argue for a broader view on the synergies of behavioral and design science research with an emphasis on the greater role that behavioral science can take in shaping and validating design science research and motivating future research. We use the INSOLVE program of research as a proof of concept for how this synergistic relationship can be leveraged.     

Web Analytics中的隐私保护问题

Web Analytics作为基于数据的的对网站建设与优化的量化分析,数据在Web Analytics中占有很地位。而这些私人性很强的数据如何才能很好的保护其隐私性,这是一个值得我们关注的问题。