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The purpose of this paper is to build and test theory regarding how rivalry in environmental management (EM) affects a focal firm’s environmental image and financial performance.

The theory is tested with an original panel data set of 2,776 focal-rival dyad pairs. Measures of environmental signals are developed from content analysis of corporate sustainability reports. Environmental performance data are drawn from the Newsweek US 500 Green Rankings database. Financial performance data are drawn from COMPUSTAT.

The main findings are that focal firm signals have a positive and significant impact on both focal firm environmental image and financial performance. Rival firm signals have a negative effect on focal firm environmental image. Surprisingly, rival firm signals have a positive impact on focal firm financial performance.

This paper can serve as a testament to the value of monitoring rival firm strategies and signaling to counter the impact of rival signals in the environmental domain. Environmental practices can be a source of competitive advantage for firms, and failure to compete in this space can place the firm at a competitive disadvantage.

This study makes several contributions to the EM literature. Leveraging competitive dynamics and the institutional viewpoints, this study builds theory with regard to how signals of competitive EM activity among a focal firm and its rivals affect environmental image and financial performance.

Content analysis is a methodology that has been used in many academic disciplines as a means to extract quantitative measures from textual information. The purpose of this paper is to document the use of content analysis in the supply chain literature. The authors also discuss opportunities for future research.

The authors conduct a literature review of 13 leading supply chain journals to assess the state of the content analysis-based literature and identify opportunities for future research. Additionally, the authors provide a general schema for and illustration of the use of content analysis.

The findings suggest that content analysis for quantitative studies and hypothesis testing purposes has rarely been used in the supply chain discipline. The research also suggests that in order to fully realize the potential of content analysis, future content analysis research should conduct more hypothesis testing, employ diverse data sets, utilize state-of-the-art content analysis software programs, and leverage multi-method research designs.

The current research synthesizes the use of content analysis methods in the supply chain domain and promotes the need to capitalize on the advantages offered by this research methodology. The paper also presents several topics for future research that can benefit from the content analysis method.

This study aims to examine the extent to which a buying firm can leverage the firm's supplier's innovations to boost the firm's own innovation performance and key moderators to this relationship. Grounded in social embeddedness theory, the authors explore the role of dyadic embeddedness between a buyer and supplier as a facilitator of buyer innovation.

Negative binomial regression was used to empirically analyze a large sample of dyadic observations from the USA manufacturing industry. Measures were developed from data acquired from Compustat, LexisNexis and Bloomberg.

The findings indicate that supplier innovation has a positive impact on a buyer firm's innovation output, particularly when the firms are technically similar and when there is a higher degree of financial interdependence in the buyer–supplier dyad.

This study provides important insights into how supplier firms can facilitate buyer innovation as and how relational factors suggested by social embeddedness theory act to strengthen this effect. Through a theoretical-based empirical examination of supply chain dyads, the findings highlight the importance of financial interdependence and technical similarity when buyers seek to benefit from supplier innovation capabilities.

The purpose of this paper is to adopt and contribute to the further development of the relational view by examining the drivers of retailer-supplier collaboration and its effect on the performance of both the retailer and the supplier.

The paper draws its conclusions from a structural analysis of dyadic survey data collected from consumer packaged goods suppliers and retailers in Brazil. In addition, objective retailer performance measures (retailer in-stock performance) are included in the data set.

The results indicate that a supplier's customer orientation is an important determinant of supplier relationship-specific investments and, ultimately, supply chain collaboration. The empirical results also indicate that retailers stand to benefit the most from a supplier's collaborative efforts. In addition, there is evidence that a supplier's customer orientation is positively related to its own performance.

The limited sample size – a result of the dyadic nature of the data – constitutes a limitation and, at the same time, presents opportunities for future, larger-scale studies. Nonetheless, this study highlights the value of customer orientation and collaboration in terms of driving performance outcomes for both suppliers and buyers, while invoking the notion that the benefits of supply chain collaboration accrue differentially over time from the retailers’ and suppliers’ perspectives.

While many of the relationships set forth in this research have been implicitly assumed by proponents of the relational view, this study furthers the development of the relational view by explicitly modeling supplier relationship-specific investments and customer orientation as antecedents of collaboration. Moreover, the study contributes to the literature on buyer-supplier collaboration by simultaneously exploring to what extent both suppliers and retailers derive benefits from such collaboration.

The purpose of this paper is to assess the current state of implementation of lean production practices in China as compared to the USA. Moreover, an institutional‐theoretic framework is developed that explores the interplay among economic, socio‐cultural and regulative forces that may shape the adoption process of lean production practices in China.

The paper draws its conclusions from an analysis of survey data from samples of Chinese and US manufacturing executives. Lean production implementation is measured via a survey instrument, and the data are analyzed via regression analysis.

The results suggest that the degree of implementation of lean production in China is equal to, if not greater than lean production implementation in the USA. While the results are fairly consistent across industries, they vary across different lean production practice bundles. In light of these findings, an institutional theory perspective is adopted to develop further insight into the potential drivers of and barriers to lean production implementation in China. It is argued that, while several economic factors function as enablers for the implementation of these practices, various social processes and cultural traits in China still hinder the full adoption of lean production.

Larger‐scale empirical studies are required for further hypothesis testing and enhanced validity. In particular, the explicit measurement of institutional forces and the statistical analysis of their effects on lean production adoption are recommended for future research.

This is the first study to systematically compare the adoption of lean practices in China and the USA. The analyses and discussions provide a basis for further theory building and hypothesis testing research. In addition, the insights offered in this study may help firms gain a better understanding of the unique opportunities and challenges associated with adoption of lean production in China.

The purpose of this paper is to: first, provide a systematic review of the drivers of retail on-shelf availability (OSA) that have been scrutinized in the literature; second, identify areas where further scrutiny is needed; and third, critically reflect on current conceptualizations of OSA and suggest alternative perspectives that may help guide future investigations.

A systematic approach is adopted wherein nine leading journals in logistics, supply chain management, operations management, and retailing are systematically scanned for articles discussing OSA drivers. The respective journals’ websites are used as the primary platform for scanning, with Google Scholar serving as a secondary platform for completeness. Journal articles are carefully read and their respective relevance assessed. A final set of 73 articles is retained and thoroughly reviewed for the purpose of this research. The systematic nature of the review minimizes researcher bias, ensures reasonable completeness, maximizes reliability, and enables replicability.

Five categories of drivers of OSA are identified. The first four – i.e., operational, behavioral, managerial, and coordination drivers – stem from failures at the planning or execution stages of retail operations. The fifth category – systemic drivers – encompasses contingency factors that amplify the effect of supply chain failures on OSA. The review also indicates that most non-systemic OOS could be traced back to incentive misalignments within and across supply chain partners.

This research consolidates past findings on the drivers of OSA and provides valuable insights as to areas where further research may be needed. It also offers forward-looking perspectives that could help advance research on the drivers of OSA. For example, the authors invite the research community to revisit the pervasive underlying assumption that OSA is an absolute imperative and question the unidirectional relationship that higher OSA is necessarily better. The authors initiate an open dialogue to approach OSA as a service-level parameter, rather than a maximizable outcome, as indicated by inventory theory.

The World Wide Web has undergone a rapid transition from the originally static hypertext to an ubiquitous hypermedia system. Today, the Web is not only used as a basis for distributed applications (Web applications), moreover it serves as a generic architecture for autonomous applications and services. Many research work has been done regarding the modeling and engineering process of Web applications and various platforms, frameworks and development kits exist for the efficient implementation of such systems. Concerning the modeling process, many of the published concepts try to merge traditional hypermedia modeling with techniques from the software engineering domain. Unfortunately, those concepts which capture all facets of the Web’s architecture become rather bulky and are eventually not applicable for a model‐driven Web application development. Moreover, there is a need for frameworks which address both, the modeling process and the implementation task and allow a model driven, semi‐automatic engineering process using CASE tools. This paper outlines the DaVinci Web Engineering Framework which supports the modeling as well as the semi‐automated implementation of Web applications. The DaVinci Architectural Layer specifies a persistent, hierarchical GUI model and a generic interaction scheme. This allows the elimination of the hypermedia paradigm, which turned out to be rather practical when building Web applications.

Big Data introduces high amounts and new forms of structured, unstructured and semi-structured data into the field of accounting and this requires alternative data management and reporting methods. Generating insights from these new data sources highlight the need for different and interactive forms of visualization in the field of visual analytics. Nonetheless, a considerable gap between the recommendations in research and the current usage in practice is evident. In order to understand and overcome this gap, a detailed analysis of the status quo as well as the identification of potential barriers for adoption is vital. The paper aims to discuss this issue.

A survey with 145 business accountants from Austrian companies from a wide array of business sectors and all hierarchy levels has been conducted. The survey is targeted toward the purpose of this study: identifying barriers, clustered as human-related and technological-related, as well as investigating current practice with respect to interactive visualization use for Big Data.

The lack of knowledge and experience regarding new visualization types and interaction techniques and the sole focus on Microsoft Excel as a visualization tool can be identified as the main barriers, while the use of multiple data sources and the gradual implementation of further software tools determine the first drivers of adoption.

Due to the data collection with a standardized survey, there was no possibility of dealing with participants individually, which could lead to a misinterpretation of the given answers. Further, the sample population is Austrian, which might cause issues in terms of generalizing results to other geographical or cultural heritages.

The study shows that those knowledgeable and familiar with interactive Big Data visualizations indicate high perceived ease of use. It is, therefore, necessary to offer sufficient training as well as user-centered visualizations and technological support to further increase usage within the accounting profession.

A lot of research has been dedicated to the introduction of novel forms of interactive visualizations. However, little focus has been laid on the impact of these new tools for Big Data from a practitioner’s perspective and their needs.

To advance the learning of professional practices in teacher education and medical education, this conceptual paper aims to introduce the idea of representational scaffolding for digital simulations in higher education.

This study outlines the ideas of core practices in two important fields of higher education, namely, teacher and medical education. To facilitate future professionals’ learning of relevant practices, using digital simulations for the approximation of practice offers multiple options for selecting and adjusting representations of practice situations. Adjusting the demands of the learning task in simulations by selecting and modifying representations of practice to match relevant learner characteristics can be characterized as representational scaffolding. Building on research on problem-solving and scientific reasoning, this article identifies leverage points for employing representational scaffolding.

The four suggested sets of representational scaffolds that target relevant features of practice situations in simulations are: informational complexity, typicality, required agency and situation dynamics. Representational scaffolds might be implemented in a strategy for approximating practice that involves the media design, sequencing and adaptation of representational scaffolding.

The outlined conceptualization of representational scaffolding can systematize the design and adaptation of digital simulations in higher education and might contribute to the advancement of future professionals’ learning to further engage in professional practices. This conceptual paper offers a necessary foundation and terminology for approaching related future research.