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The purpose of this paper is to show that forgotten classics, such as Melvin T. Copeland’s (1924) Principles of Merchandising, can still teach lessons to students of the history of marketing thought.

The method involved using various key words on several internet search engines. The extensive internet search produced more than a dozen contemporaneous reviews and commentaries. Additionally, there was an intensive search through the histories of marketing thought literature. The extensive and intensive searches allowed a meta-analysis reexamining Copeland’s principles in light of future historical developments from the mid-1920s to the 21st century.

Historically, Copeland’s principles established the commodity school of marketing thought. (One of the three traditional approaches to understanding marketing taught to generations of students from the mid-1920s until the mid-1960s.) Although the traditional approaches/schools have long gone out of favor, Copeland’s classification of consumer and industrial (business) goods (products and services) have stood the test of time and are still in use 100 years later. Long overlooked, Copeland’s (1924) Principles of Merchandising also anticipated the marketing management/strategy as well as the consumer/buyer behavior schools of marketing thought, dominant in the discipline since the 1960s, for which he has seldom – if ever – been acknowledged.

Historical research is limited because some relevant source material may no longer exist or may have been overlooked.

There have been no reviews of Copeland’s principles in almost a century, and no published meta-analysis of this forgotten classic exists. New discoveries reveal the value in studying marketing history and the history of marketing thought. For marketing as a social science to progress, it is invaluable to understand how ideas originated, were improved and integrated into larger conceptualizations, classification schema and theories over time.

This chapter examines China’s corporate governance and accounting environment that shapes the adoption of internationally acceptable principles and standards. Specifically, it examines international influences, including supranational organizations; foreign investors and international accounting firms; domestic institutional influences, including the political system, economic system, legal system, and cultural system; and accounting infrastructure. China’s convergence is driven by desired efficiency of the corporate sector and legitimacy of participating in the global market. Influenced heavily by international forces in the context of globalization, corporate governance and accounting practices are increasingly becoming in line with internationally acceptable standards and codes. While convergence assists China in obtaining legitimacy, improving efficiency is likely to be adversely affected given that corporate governance and accounting in China operate in an environment that differs considerably from those of Anglo-American countries. An examination of the corporate governance and accounting environment in China suggests heavy government involvement within underdeveloped institutions. While the Chinese government has made impressive progress in developing the corporate governance and accounting environment for the market economy, China’s unique institutional setting is likely to affect how the imported concepts are interpreted and implemented.

This paper aims to explore the impact of product market competition (PMC) on companies’ investment in external auditing.

This paper applies a conceptual framework derived from demand–supply analysis and a panel data set of 2,263 listed manufacturing companies in China covering the period 2012–2019. In the assessment of PMC, this study measures industry-level competition intensity and company-specific market power separately.

Industries appear to engage in a lower average level of external auditing if industrial competition intensity is either too high or too low. Similarly, companies spend less on external auditing if their market power is either too strong or too weak, and the company-level inverted U-shaped relationship is much more evident in industries with weak PMC.

This paper shows that a company’s external audit strategy is affected by the level of competition it faces in its market. The findings of this paper can improve the current linear PMC–auditing theoretical framework and provide insights into the strategic auditing of listed companies in China. The findings also have significant implications for policy recommendations regarding corporate governance and market scrutiny regulations.

Quality control of products and services has been recognised in China as an important mainspring for economic reform and growth. The recently implemented consultation and reward programme for quality improvement has had some positive impact. But effective quality control systems in individual enterprises along with modern management methods still need to be properly established. China welcomes external technical exchanges. In an international co‐operative research and development project we studied computer‐interactive auditing and international quality programme registration. This is to induce and guide management towards improved quality and exporting capacity.

The purpose of this paper is to analyze the distribution strategies implemented by foreign firms in emerging markets, and to investigate whether they represent an opportunity for firms to innovate their practice. China is selected as the setting of the investigation as distribution is a critical determinant of business success for international firms operating there.

A multiple-case study approach is adopted by investigating the distribution strategies of four Italian firms in China. The collected data consist of interviews with firm managers and their distributors. To ensure triangulation and cross-verify the findings from the primary data, secondary data consisting of sector reports and newspaper articles were analyzed.

Results discuss how foreign firms develop their distribution system in China and suggest that emerging markets can enable reverse innovation in their distribution strategies.

The research suffers from the limitations of the generalizability of the findings as the study was carried out on a restricted number of firms, and it considered their strategies in one single market.

Managerial implications are discussed on the extent to which the Chinese distribution system still represents a key issue for foreign firms, but it also provides with opportunity for innovation.

While previous research on innovation in emerging markets has mainly focused on product innovation, this study suggests some areas for distribution innovation.

The purpose of this paper is to report the findings from a research project aiming to investigate the implications of digitization for the Australian book publishing sector and in particular for current and emerging business models. Recognizing the dynamic nature of the publishing industry in Australia with the rapid development of information and communication technologies, this study aims to address several critical research questions in the publishing industry in Australia as follows: what are the major trends emerging within the publishing industry in Australia? What implications do these trends have for business models? What technologies and applications offer the greatest potential for supporting value creation activities and business model development in the digital era?

A comprehensive review of the literature on business models is conducted, in particular, in relation to the publishing industry. The current status of the technology used in the publishing industry is explored, and the overall profile of the Australian publishing industry is established with the use of a document analysis method based on secondary data available. This leads to the development of an online questionnaire for surveying the book publishers in Australia, and results in the conduct of 14 follow‐up case studies, the latter forming the basis for the current paper.

The findings are that global trends towards consolidation and increasing competition are replicated in Australia, and that business models are responding, for example, with the emergence of hybrid models, embodying traditional elements and those consequent on the advent of digitization. It is also clear that the internet and related network technologies, along with content and digital rights management technologies are emerging as key to change, but those strategic decisions are ultimately based on business drivers rather than on emerging technologies.

The research findings would be of significant benefits to Australian business, especially, the publishers in Australia in their endeavour to effectively deal with the challenges that the rapid advance in information and communication technologies has brought to every organization. These findings help organizations, including Australian publishers, realistically assess the impact of the digital technologies on their businesses and subsequently formulate effective policies and strategies for fully making use of the potential of the digital technologies for developing and sustaining their competitive advantages in the market.

The originality of this study is the use of a multi‐method approach for investigating the implications of digitization for the Australian book publishing sector and in particular for current and emerging business models. Such a study is of tremendous value to the Australian publishers due to the rapid advance of digital technologies; the need for a better understanding of the impacts of such technologies advances on publishers; and the increasing competitive market in publishing. The research findings would help Australian publishers and also other organizations develop specific strategies and polices for effectively dealing with the challenges from the rapid development of the digital technologies.

The purpose of this paper is to examine the impacts of farmland renting-in on planted grain acreage.

A survey data of five counties were analyzed with the two-stage ordinary least squares model.

Households renting-in land trended to plant more maize, and the more land was rented by a household the more maize was planted, while wheat acreage showed non-response to farmland renting-in.

Overall, the analysis suggests that policy makers should be prepared for different changing trends of grain crop acreage across the nation as farmland transfer continues. Future research should pay attention to the effect of farmland transfer on agricultural productivity and rural household income growth.

As the Chinese Government is promoting larger-scale and more mechanized farms as a way of protecting grain security, it is important to understand whether farmland renting-in will reduce planted grain acreage. This study provides empirical evidence showing the answer to that question may differ across different regions and depend on the particular grain crop in question.

Internet technology allows millions of people to find high quality medical resources online, with the result that personal healthcare and medical services have become one of the fastest growing markets in China. Data relating to healthcare search behavior may provide insights that could lead to better provision of healthcare services. However, discrepancies often arise between terminologies derived from professional medical domain knowledge and the more colloquial terms that users adopt when searching for information about ailments. This can make it difficult to match healthcare queries with doctors’ keywords in online medical searches. The paper aims to discuss these issues.

To help address this problem, the authors propose a transfer learning using latent factor graph (TLLFG), which can learn the descriptions of ailments used in internet searches and match them to the most appropriate formal medical keywords.

Experiments show that the TLLFG outperforms competing algorithms in incorporating both medical domain knowledge and patient-doctor Q&A data from online services into a unified latent layer capable of bridging the gap between lay enquiries and professionally expressed information sources, and make more accurate analysis of online users’ symptom descriptions. The authors conclude with a brief discussion of some of the ways in which the model may support online applications and connect offline medical services.

The authors used an online medical searching application to verify the proposed model. The model can bridge users’ long-tailed description with doctors’ formal medical keywords. Online experiments show that TLLFG can significantly improve the searching experience of both users and medical service providers compared with traditional machine learning methods. The research provides a helpful example of the use of domain knowledge to optimize searching or recommendation experiences.

The authors use transfer learning to map online users’ long-tail queries onto medical domain knowledge, significantly improving the relevance of queries and keywords in a search system reliant on sponsored links.

The K-means (KM) clustering algorithm is extremely responsive to the selection of initial centroids since the initial centroid of clusters determines computational effectiveness, efficiency and local optima issues. Numerous initialization strategies are to overcome these problems through the random and deterministic selection of initial centroids. The random initialization strategy suffers from local optimization issues with the worst clustering performance, while the deterministic initialization strategy achieves high computational cost. Big data clustering aims to reduce computation costs and improve cluster efficiency. The objective of this study is to achieve a better initial centroid for big data clustering on business management data without using random and deterministic initialization that avoids local optima and improves clustering efficiency with effectiveness in terms of cluster quality, computation cost, data comparisons and iterations on a single machine.

This study presents the Normal Distribution Probability Density (NDPD) algorithm for big data clustering on a single machine to solve business management-related clustering issues. The NDPDKM algorithm resolves the KM clustering problem by probability density of each data point. The NDPDKM algorithm first identifies the most probable density data points by using the mean and standard deviation of the datasets through normal probability density. Thereafter, the NDPDKM determines K initial centroid by using sorting and linear systematic sampling heuristics.

The performance of the proposed algorithm is compared with KM, KM++, Var-Part, Murat-KM, Mean-KM and Sort-KM algorithms through Davies Bouldin score, Silhouette coefficient, SD Validity, S_Dbw Validity, Number of Iterations and CPU time validation indices on eight real business datasets. The experimental evaluation demonstrates that the NDPDKM algorithm reduces iterations, local optima, computing costs, and improves cluster performance, effectiveness, efficiency with stable convergence as compared to other algorithms. The NDPDKM algorithm minimizes the average computing time up to 34.83%, 90.28%, 71.83%, 92.67%, 69.53% and 76.03%, and reduces the average iterations up to 40.32%, 44.06%, 32.02%, 62.78%, 19.07% and 36.74% with reference to KM, KM++, Var-Part, Murat-KM, Mean-KM and Sort-KM algorithms.

The KM algorithm is the most widely used partitional clustering approach in data mining techniques that extract hidden knowledge, patterns and trends for decision-making strategies in business data. Business analytics is one of the applications of big data clustering where KM clustering is useful for the various subcategories of business analytics such as customer segmentation analysis, employee salary and performance analysis, document searching, delivery optimization, discount and offer analysis, chaplain management, manufacturing analysis, productivity analysis, specialized employee and investor searching and other decision-making strategies in business.