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This papers aims to study the influence of water absorption on the mechanical properties of poly lactic acid (PLA) and PLA/Wood composites. Virgin PLA and PLA/Wood double-bone-shaped specimens were prepared by two methods: injection moulding and 3D printing. The results were compared to each other and showed the influence of the production method on the properties of the produced parts.

Morphology studies were done by scanning electron microscopy (SEM) from fracture surfaces of tensile and notched impact specimens of all samples. Tensile properties were analysed by the production and testing of dog-bone-shaped samples. Heat deflection temperature (HDT) was tested, as also was the crystallinity of the tested samples by differential scanning calorimetry.

The values for notched impact strength were higher upon water uptake in the case of injection-moulded specimens, which was not the case with 3D-printed specimens. Tensile properties of the specimens produced by both methods were reduced after water absorption tests. Values of the HDT were also lower after water absorption tests studied for both processing methods.

Morphology studies were done by SEM from fracture surfaces of tensile as well as notched impact specimens of injection-moulded and 3D-printed samples. The effect of water storage on various samples was tested. The two different production technologies were compared to each other owing to their influence of water storage. This study also dealt with NFC compounds and produced NFC composites and the influence of water storage on these samples.

The purpose of this paper is to analyze the performance of incubators in the stages of formation and development of incubated business networks, especially in bottom-up and top-down network models.

The research is defined as qualitative and descriptive, with the application of multiple case studies, in which two networks of incubated businesses were investigated, one being top-down and the other bottom-up, which emerged within the incubation process of two business incubators (CIETEC and INCIT). To make the study operational, 11 semi-structured interviews were carried out and the thematic analysis of content was developed.

The results pointed out that in the top-down network the incubator performs a new assignment, the network orchestration, which corresponds to the actions of formation, coordination and governance of the group. In the bottom-up network, it was found that the role of the incubator was to expand the value offers usually practiced.

As a limitation of the research, the very limitation of case studies is pointed out that is they do not allow for generalizations.

The research contributes to reflections on the effectiveness of the incubator and sheds light on the complementarity of networks in incubation processes, providing gains for incubators, incubated businesses and society.

The originality of this document is the new role of the incubator, which is orchestration, and its categorization. The results allow us to understand the effects of providing networks and relationships for incubated businesses. In addition, this study broadens the focus of traditional analyses of the incubator–incubated duo to consider the incubator–network–incubated trio.

The data economy, emerging from the current hyper-technological landscape, is a global digital ecosystem where data is gathered, organized and exchanged to create economic value. This paper aims to shed light on the interplay of the different topics involved in the data economy, as found in the literature. The study research provides a comprehensive understanding of the opportunities, challenges and implications of the data economy for businesses, governments, individuals and society at large, while investigating its impact on business value creation, knowledge and digital business transformation.

The authors conducted a literature review that generated a conceptual map of the data economy by analyzing a corpus of research papers through a combination of machine learning algorithms, text mining techniques and a qualitative research approach.

The study findings revealed eight topics that collectively represent the essential features of data economy in the current literature, namely (1) Data Security, (2) Technology Enablers, (3) Business Implications, (4) Social Implications, (5) Political Framework, (6) Legal Enablers, (7) Privacy Concerns and (8) Data Marketplace. The study resulting model may help researchers and practitioners to develop the concept of data economy in a structured way and provide a subset of specific areas that require further research exploration.

Practically, this paper offers managers and marketers valuable insights to comprehend how to manage the opportunities deriving from a constantly changing competitive arena whose value is today also generated by the data economy.

Socially, the authors also reveal insights explaining how the data economy features may be exploited to build a better society.

This is the first paper exploring the data economy opportunity for business value creation from a critical perspective.

Digitally intermediated peer-to-peer exchanges have accelerated in occurrence, and as a consequence, they have introduced an increased pluralism of connotations. Accordingly, this paper aims to assess user perceptions of the interplay between the sharing, access, platform, and community-based economies.

The sharing, access, platform, and community-based economies have been systematically tracked in the social media landscape using Social Media Analytics (SMA). In doing so, a total material of 62,855 publicly posted user-generated content concerning the four respective economies were collected and analyzed.

Even though the sharing economy has been conceptually argued to be interlinked with the access, platform, and community-based economies, the empirical results of the study do not validate this interlinkage. Instead, the results regarding user perceptions in social media show that the sharing, access, platform, and community-based economies manifest as clearly separated.

This paper contributes to existing literature by offering an empirical validation, as well as an in-depth understanding, of the sharing economy's interlinkage to other economies, along with the extent to which the overlaps between these economies manifest in social media.

Applications powered by artificial intelligence (AI) and machine learning (ML) have become a crucial factor for success in modern sales organizations. This chapter investigates how Salesforce achieves scalable AI for businesses of all sizes and explores sales applications of AI and machine learning that are most common across industries. It is divided into three sections. The first section gives an introduction to AI and machine learning. The second section shows how data and automated machine learning models provide the foundation for AI applications and explains how Salesforce achieves scalable AI and machine learning for business applications. The third section demonstrates how AI applications impact the modern sales organization and the work of sales representatives. AI does not replace humans; it allows sales organizations to better engage with prospects and customers. Sales representatives using AI outperform their counterparts that rely purely on traditional methods.

Research on biases in investment decision-making is indubitably important; however, studies in this context are relatively scarce. Unpacking bias has received attention in the psychological literature yet very little attention from management accounting research. This bias suggests that the perceived probability that an event will occur generally increases when the event's description is unpacked into a disjunction of subevents. The authors hypothesize that for a capital investment decision context, managers' judgement of the probability of a future event depends on whether the event is described as one packed event or is unpacked into several disjoint subevents. Additionally, the authors propose that altering the format of the description of an event's occurrence from percentage values to relative frequencies reduces unpacking bias.

To test the study’s hypotheses, the authors conducted two experiments based on a 3 × 2 mixed experimental design in which manager participants were asked to estimate the failure probabilities of technical systems in the context of an investment decision.

The authors provide evidence that unpacking bias occurs in an investment scenario, which can be characterized as a high-stakes decision context. Changing the format in which probabilities are presented from percentage values to relative frequencies significantly reduces the bias.

Additional instructions did not further reduce unpacking bias.

For investment decisions under uncertainty, performance indicators in management templates should be presented in relative frequencies to improve managerial decision-making. The fact that the authors could not show an additional effect of instructions in management accounting reports indicates that it is challenging for management accountants to reduce the biased decision-making of managers by “teaching” them through the provision of instructions.

The authors contribute to accounting research by illustrating unpacking bias and by deriving a debiasing mechanism in a capital investment decision context.

In the field of business, digital transformation is the integration of digital technology into all areas of business, from generating to deliver value to customers. This concept is essential for sustainable growth of a company and its overall economy. Based on this fact, this authentic and informative research is conducted whose major aim is to examine the importance of digital transformation within a business through big data, the Internet of things and blockchain-based capabilities for overall strategic performance within the telecom sector in China.

For that aim, data quality and technology competence are considered as independent variables, strategic performance as dependent variable and big data analytics capabilities, Internet of things capabilities and blockchain capabilities routinization acted as mediators within this paper. In its data collection mechanism, an online survey was conducted in which questionnaires are randomly distributed to the telecom sector's professionals in which only 343 of them gave their valid outcomes. After collecting primary data, confirmatory factor analysis (CFA) and structural equation modeling (SEM)–based statistical outcomes have been generated.

Results indicate that there is a significant relationship between data quality and strategic performance and between technological competence and strategic performance. Also, the big data analytics and Internet of Things capabilities acted as significant mediating role between both independent and dependent variables. But blockchain capabilities routinization is that variable that acts as an insignificant mediator between independent and dependent variables' relationship.

Overall, this study is an informative and attractive source for the Chinese government, its telecom industry, administrative body and related ones to understand the importance of such IT capabilities' implications within their operating activities for their strategic performance management. Also, related field scholars can utilize its reliable data in their research analysis. Its major limitations are (1) lack of qualitative/ mixed method of research and (2) lack of comparative analysis that may impact the acceptability factor of this paper, and this weakness can be overcome by upcoming scholars in their research.

This study illustrates the economic and financial dynamics of the sector, analysing the evolution of the main ratios of profitability and financial structure of 1,559 Italian real estate companies divided into the three macro-regions: North, Centre and South, in the period 2011–2020. In this way, it is also possible to verify the responsiveness to the 2020 pandemic crisis.

The analysis uses descriptive statistics tools and the ANOVA method of analysis of variance, supplemented by the Tukey–Kramer test, to identify significant differences between the three Italian macro-regions.

The study shows the increase in profitability after the 2008 crisis, despite its reverberation in the years 2012–2013. The financial structure of companies improved almost everywhere. The pandemic had modest effects on performance.

In the future, other indices should be considered to gain a more comprehensive view. This is a quantitative study based on financial statements data that neglects other important economic and social factors.

Public policies could use this study for better interventions to support the sector. In addition, internal management can compare their company's performance with the industry average to identify possible improvements.

The research analyses an economic field that employs a large number of people, especially when considering the construction and real estate services covered by this analysis.

The study contributes to the literature by providing a quantitative analysis of industry dynamics, with comparative information that can be deduced from financial statements over the years.

The recent COVID-19 is forcing governments to implement policies on a large scale to counter its spread. A central issue in the economic debate is the effective quantification of the impact that the policies may implicitly have on the economy. This study quantifies the effects of lockdown in the United States.

The study uses a dynamic computable general equilibrium (DCGE) model calibrated on a social accounting matrix (SAM). The lockdown policy is applied on the supply side, by using a reduction in the production according to the closing time of each industry. The reduction in the demand is also applied, throughout the contraction of the household consumption that is diversified by the commodities. In order to analyse the pure effect of the lockdown policy, the interventions by the policy makers are not considered in this study.

The results show an important contraction of productivity in the food industry, the real estate activities, the constructions and the general services.

The contraction produces a fall of the GDP for the whole period analysed, traced by the investments, which includes repercussions on the whole productive system, employment and income of the institutional sectors.

Describes the scheduling problem and JOB, then presents an extensive job shop scheduling session in which a variety of scheduling problems are encountered and overcome using JOB′s interactive scheduling option. The example shows how work orders may be created and scheduled, and the schedules evaluated, all within the framework of the JOB system. By working with typical job shop scheduling opportunities in a realistic though simulated environment, users will better understand the problems job shop schedulers actually face and will be better able to solve them.