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Fused deposition modeling (FDM) is an extensively used additive manufacturing method with the capacity to build complex functional components. Due to the machinery and environmental factors during manufacturing, the FDM parts inevitably demonstrated uncertainty in properties and performance. This study aims to identify the stochastic constitutive behaviors of FDM-fabricated polylactic acid (PLA) tensile specimens induced by the manufacturing process.

By conducting the tensile test, the effects of the printing machine selection and three major manufacturing parameters (i.e., printing speed S, nozzle temperature T and layer thickness t) on the stochastic constitutive behaviors were investigated. The influence of the loading rate was also explained. In addition, the data-driven models were established to quantify and optimize the uncertain mechanical behaviors of FDM-based tensile specimens under various printing parameters.

As indicated by the results, the uncertain behaviors of the stiffness and strength of the PLA tensile specimens were dominated by the printing speed and nozzle temperature, respectively. The manufacturing-induced stochastic constitutive behaviors could be accurately captured by the developed data-driven model with the R² over 0.98 on the testing dataset. The optimal parameters obtained from the data-driven framework were T = 231.3595 °C, S = 40.3179 mm/min and t = 0.2343 mm, which were in good agreement with the experiments.

The developed data-driven models can also be integrated into the design and characterization of parts fabricated by extrusion and other additive manufacturing technologies.

Stochastic behaviors of additively manufactured products were revealed by considering extensive manufacturing factors. The data-driven models were proposed to facilitate the description and optimization of the FDM products and control their quality.

The advancements in internet technologies and the use of sophisticated digital devices in supply chain operations incessantly generate enormous amounts of data, which is termed as big data (BD). The BD technologies have brought about a paradigm shift in the supply chain decision-making towards profitability and sustainability. The aim of this work is to address the issue of implementation of the big data analytics (BDA) in sustainable supply chain management (SSCM) by identifying the relevant factors and developing a structural model for this purpose.

Through a comprehensive literature review and experts’ opinion, the crucial factors are found using the PESTEL framework, which covers political, economic, social, technological, environmental and legal factors. The structural model is developed based on the results of the total interpretive structural modelling (TISM) procedure and MICMAC analysis.

The policy support regarding IT, culture of data-based decision-making, inappropriate selection of BDA technologies and the laws related to data security and privacy are found to affect most of the other factors. Also, the company’s vision towards environmental performance and willingness for material and energy optimization are found to be crucial for the environmental and social sustainability of the supply chain.

The study is focused on the manufacturing supply chain in emerging economies. It may be extended to other industry sectors and geographical areas. Also, additional factors may be included to make the model more robust.

The proposed model imparts an understanding of the relative importance and interrelationship of factors. This may be useful to managers to assess their strengths and weaknesses and ascertain their priorities in the context of their organization for developing a suitable investment plan.

The study establishes the importance of BDA for conservation and management of energy and material. This is crucial to develop strategies for enhancing eco-efficiency of the supply chain, which in turn enhances the economic returns for the society.

This study addresses the implementation of BDA in SSCM in the context of emerging economies. It uses the PESTEL framework for identifying the factors, which is a comprehensive framework for strategic planning and decision-making. This study makes use of the TISM methodology for model development and deliberates on the social and environmental implications too, apart from theoretical and managerial implications.

This study aims to focus on the five most relevant and discursive emerging technologies in accounting (cloud computing, big data and data analytics, blockchain, artificial intelligence (AI) and robotics process automation [RPA]). It investigates the adoption and use of these technologies based on data collected from accounting professionals in a technology-developed country – Canada, through a survey.

The study investigates the adoption and use of emerging technologies based on data collected from accounting professionals in a technology-developed country – Canada, through a survey. This study considers the said nature and characteristics of emerging technologies and proposes a model using the factors that have been found to be significant and most commonly investigated by existing prior technology-organization-environment (TOE)-related technology adoption studies. This survey applies the TOE framework and examines the influence of significant and most commonly known factors on Canadian firms’ intention to adopt the said emerging technologies.

Study results indicate that Canadian accounting professionals’ self-assessed knowledge (about these emerging technologies) is more theoretical than operational. Cloud computing is highly used by Canadian firms, while the use of other technologies, particularly blockchain and RPA, is reportedly low. However, firms’ intention about the future adoption of these technologies seems positive. Study results reveal that only the relative advantage and top management commitment are found to be significant considerations influencing the adoption intention.

Study findings confirm some results presented in earlier studies but provide additional insights from a new perspective, that of accounting professionals in Canada. The first limitation relates to the respondents. Although accounting professionals provided valuable insights, their responses are personal views and do not necessarily represent the views of other professionals within the same firm or the official position of their accounting departments or firms. Therefore, the exclusion of diverse viewpoints from the same firm might have negatively impacted the results of this study. Second, this study sample is limited to Canada-based firms, which means that the study reflects only the situation in that country. Third, considering the research method and the limit on the number of questions the authors could ask, respondents were only asked to rate the impact of these five technologies on the accounting field and to clarify which technologies are used.

This study’s findings confirm that the organizational intention to adopt new technology is not primarily based on the characteristics of the technology. In the case of emerging technology adoption, the decision also depends upon other factors related to the internal organization. Furthermore, although this study found no support for the effect of environmental factors, it fills a gap in the literature by including the factor of vendor support, which has received little attention in prior information technology (IT)/ information system (IS) adoption research. Moreover, in contrast to most prior adoption studies, this study elaborates on accounting professionals’ experience and perceptions in investigating the organizational adoption and use of emerging technologies. Thus, the findings of this study are valuable, providing insights from a new perspective, that of professional accountants.

The study findings may serve as a guide for researchers, practitioners, firms and other stakeholders, particularly technology providers, interested in learning about emerging technologies’ adoption and use in Canada and/or in a relevant context. Contrary to most prior adoption studies, this study elaborates on accounting professionals’ experience and perceptions in investigating the organizational adoption and use of emerging technologies. Thus, the findings of this study are valuable, providing insights from a new perspective, that of professional accountants.

The study provides insights into the said technologies’ actual adoption and improves the awareness of firms and stakeholders to the effect of some constructs that influence the adoption of these emerging technologies in accounting.

The objective of this paper is to further the understanding of netnography as a research method for supply chain academics. Netnography is a method for gathering and gaining insight from industry-specific online communities. We prescribe that viewing netnography through the lens of the supply chain will permit researchers to explore, discover, understand, describe or report concepts or phenomena that have previously been studied via survey research or quantitative modeling.

To introduce netnography to supply chain research, we propose a framework to guide how netnography can be adopted and used. Definitions and directions are provided, highlighting some of the practices within netnographic research.

Netnography provides the researcher with another avenue to pursue answers to research questions, either alone or in conjunction with the dominant methods of survey research and quantitative modeling. It provides another tool in the researchers’ toolbox to engage practitioners in the field.

The development of netnography as a research method is associated with Robert Kozinets. He developed the method to study online communities in consumer behavior. We justify why this method can be applied to supply chain research, how to collect data and provide research examples of its use. This technique has room to grow as a supply chain research method.

This paper aims to critically examine the accounting and information systems literature to understand the changes that are occurring in the management accounting profession. The changes the authors are interested in are linked to technology-driven innovations in managerial decision-making and in organizational structures. In addition, the paper highlights research gaps and opportunities for future research.

The authors adopted a grounded theory literature review method (Wolfswinkel et al., 2013) to achieve the study’s aims.

The authors identified four research themes that describe the changes in the management accounting profession due to technology-driven innovations: structured vs unstructured data, human vs algorithm-driven decision-making, delineated vs blurred functional boundaries and hierarchical vs platform-based organizations. The authors also identified tensions mentioned in the literature for each research theme.

Previous studies display a rather narrow focus on the role of digital technologies in accounting work and new competences that management accountants require in the digital era. By contrast, the authors focus on the broader technology-driven shifts in organizational processes and structures, which vastly change how accounting information is collected, processed and analyzed internally to support managerial decision-making. Hence, the paper focuses on how management accountants can adapt and evolve as their organizations transition toward a digital environment.

This study aims to analyze the factors that drive or prevent interorganizational data sharing in the context of digital transformation (DT). Data sharing appears as a precondition for companies to capture emerging opportunities in supply chain management and for product-related servitization; however, there are ongoing concerns, and data are often perceived as the “new oil.” It is thus important to gain a better understanding of the determinants of firms’ decisions.

The authors develop an embedded case study analysis involving 16 firms within an extended supply network in the automotive industry. The authors focus on the peculiarities of the new context, as opposed to elements highlighted by research prior to the advent of the latest technologies. Abductive reasoning is applied to the theoretical foundations of the resource-based view, resource dependence theory and the complex adaptive systems perspective.

Data sharing is largely underpinned by factors identified prior to DT, such as data specificity, dependence dynamics and protection mechanisms and the dynamism of the business context. DT, however, can influence the extent of data sharing. New factors concern complementarities whenever data are pooled from different sources and digital platforms, as well as different forms of data ownership protection.

This study stresses that data sharing in the context of DT can be explained through established theoretical lenses, providing the integration of elements accounting for new technological opportunities.

Data storytelling courses position students as agents in creating stories interpreted from data about a social problem or social justice issue. The purpose of this study is to explore two research questions: What themes characterized students’ iterative development of data story topics? Looking back at six years of iterative feedback, what categories of data literacy pedagogy did instructors engage for these themes?.

This project examines six years of data storytelling final projects using thematic analysis and three years of instructor feedback. Ten themes in final projects align with patterns in feedback. Reflections on pedagogical approaches to students’ topic development suggest extending data literacy pedagogy categories – formal, personal and folk (Pangrazio and Sefton-Green, 2020).

Data storytelling can develop students’ abilities to move from being consumers to creators of data and interpretations. The specific topic of personal data exposure or risk has presented some challenges for data literacy instruction (Bowler et al., 2017). What “personal” means in terms of data should be defined more broadly. Extending the data literacy pedagogy categories of formal, personal and folk (Pangrazio and Sefton-Green, 2020) could more effectively center social justice in data literacy instruction.

Implications for practice include positioning students as producers of data interpretation, such as role-playing data analysis or decision-making scenarios.

Data storytelling has the potential to address current challenges in data literacy pedagogy and in teaching critical data literacy.

Course descriptions provide a template for future data literacy pedagogy involving data storytelling, and findings suggest implications for expanding definitions and applications of personal and folk data literacies.

This study aims to investigate how companies’ understanding of the business network influences the creation of value in business-to-business relationships. The authors do this by analysing dimensions in actors’ “network pictures” and illustrating how value perception and network understanding influence actors’ mutual effort to create value. Approaching relationship value from the point of actors’ cognitive understanding of their business network has so far been largely overlooked in relationship value research.

This study applies a qualitative case study methodology whereby dyadic data from a well-established business-to-business relationship is collected from 18 company representatives through personal interviews and group interviews supplemented by participant observations and company data.

The findings contribute with new insight into how companies’ understanding of their surrounding network influence (facilitates or limits) relationship value creation. The authors find that companies continuously reflect on changes in their networks and the related changes in partners’ value perceptions. Through value articulations, companies seek to explicitly express their value perception. Value reflections and value articulations create a dynamic process formed not only by the individual actor but also through their relationship and engagement in their network environment. This requires companies to develop their networking capabilities.

This paper presents findings, insights and contributions limited to a case study of a particular business relationship within an industrial setting. Although the findings and contributions are valid and in line with the criteria for rigorous qualitative research, the authors advocate and call for additional studies that investigate relationships value creation and address the interplay between actors’ network understanding and their actions and behaviour. One way to approach this would be to test the four propositions derived and presented as part of the present study.

The findings imply that management needs to be aware not only of the value created and delivered to a specific partner but also of how the partner’s understanding of the wider network will influence the value delivering and capturing process.

This study contributes to the growing literature on relationship value creation by outlining a dynamic process where relationship partners reflect upon and articulate value. Such activities are influenced by the partners’ network understanding and form the basis of the mutual relationship value creation effort. The findings also contribute to the network pictures literature by emphasizing insights into the formation of value perceptions through actors’ understanding of their surrounding networks.

The purpose of this paper is to investigate how different manufacturing technologies are bundled together and how these bundles influence operations performance and, indirectly, business performance. With the emergence of Industry 4.0 (I4.0) technologies, manufacturing companies can use a wide variety of advanced manufacturing technologies (AMT) to build an efficient and effective production system. Nevertheless, the literature offers little guidance on how these technologies, including novel I4.0 technologies, should be combined in practice and how these combinations might have a different impact on performance.

Using a survey study of 165 manufacturing plants from 11 different countries, we use factor analysis to empirically derive three distinct manufacturing technology bundles and structural equation modeling to quantify their relationship with operations and business performance.

Our findings support an evolutionary rather than a revolutionary perspective. I4.0 technologies build on traditional manufacturing technologies and do not constitute a separate direction that would point towards a fundamental digital transformation of companies within our sample. Performance effects are rather weak: out of the three technology bundles identified, only “automation and robotization” have a positive influence on cost efficiency, while “base technologies” and “data-enabled technologies” do not offer a competitive advantage, neither in terms of cost nor in terms of differentiation. Furthermore, while the business performance impact is positive, it is quite weak, suggesting that financial returns on technology investments might require longer time periods.

Relying on a complementarity approach, our research offers a novel perspective on technology implementation in the I4.0 era by investigating novel and traditional manufacturing technologies together.

This paper aims to study the economic effects of Cloud Computing for a sample of Israeli firms. The authors propose a framework that considers how this technology affects firm performance also introducing the indirect economic effects that take place through cloud-complementary technologies such as Big Data and Machine Learning.

The model is estimated through structural equation modeling. The data set consists of the microdata of the survey of information and communication technologies uses and cyber protection in business conducted in Israel by the Central Bureau of Statistics.

The results point to Cloud Computing as a crucial technology to increase firm performance, presenting significant direct and indirect effects as the use of complementary technologies maximizes its impact. Firms that enjoy most direct economic gains from Cloud Computing appear to be the smaller ones, although larger enterprises seem more capable to assimilate complementary technologies, such as Big Data and Machine Learning. The total effects of cloud on firm performance are quite similar among manufacturing and service firms, although the composition of the different effects involved is different.

This paper is one of the very few analyses estimating the impact of Cloud Computing on firm performance based on country microdata and, to the best of the authors’ knowledge, the first one that contemplates the indirect economic effects that take place through cloud-complementary technologies such as Big Data and Machine Learning.