We draw on outstanding recent research to substantiate factors driving pro-environmental food purchasing behavior. Throwing light on purchasing behavior for environmentally sustainable foods, our study highlights the relevance of consumer trust and motivations in organic product markets together with individuals' perceived value and willingness to buy such items throughout the choice behavior and decision-making process. Our findings prove that most aspects influencing consumers' attitudes for and choices of organic foods are related to their trust and perceptions of the nutritional benefits such products provide. The insights gained from our research extend present knowledge concerning consumer behavior and purchase intention for environmentally sustainable products. The chief gaps and issues identified by the review cover the variety of organic food consumer purchase intentions and behaviors, including the relative environmental performance of organic food production and the link between the motivational values and attitudes concerning the consumption of non-chemical products. Apart from sustainable agriculture and upsides of organic farming, the main disadvantages are as follows: recycling and aligning with natural operations does not necessitate chemical inputs, but organic food is more prohibitive as farmers do not obtain significant crop productivity out of their land, while organic goods may have a price of up to 40% more (production expenses are steeper because farmers demand more labor force), marketing and distribution are not streamlined as organic products are delivered in diminished volumes, food disorders may occur more frequent, and chemical-free agriculture cannot produce sufficient nutrients that the world's population requires to live on.

Research has identified that there is a paucity of reviews covering green public procurement (GPP) in terms of environmentally responsible behavior and sustainability policy adoption. Using, comprehensively, the most recent (2017–2020) and relevant (Web of Science- and Scopus-indexed) empirical sources, our paper fills the gap in the literature by focusing on the main developing streams of research, that is: How GPP drives the circular economy; GPP of construction and building materials; environmental and supply chain management measures in GPP; the procurement of sustainable innovation; environmental policy objectives of GPP as regards energy, pollution, carbon footprint, and climate change; GPP as an environmental policy mechanism for production and use of sustainable goods and services; and GPP as an integral component of sustainable development and performance. Further investigations can explore hot topics related to the role of GPP in the automated algorithmic decision-making processes taking place in data-driven smart sustainable cities because the harnessing, among other things, of sensing and computing technologies, network connectivity systems, and the Cognitive Internet of Things will fulfill the incessant exigencies of public administration.

In this article, we cumulate previous research findings indicating that cyber-physical production systems bring about operations shaping social sustainability performance technologically. We contribute to the literature on sustainable cyber-physical production systems by showing that the technological and operations management features of cyber-physical systems constitute the components of data-driven sustainable smart manufacturing. Throughout September 2020, we performed a quantitative literature review of the Web of Science, Scopus, and ProQuest databases, with search terms including “sustainable industrial value creation”, “cyber-physical production systems”, “sustainable smart manufacturing”, “smart economy”, “industrial big data analytics”, “sustainable Internet of Things”, and “sustainable Industry 4.0”. As we inspected research published only in 2019 and 2020, only 323 articles satisfied the eligibility criteria. By eliminating controversial findings, outcomes unsubstantiated by replication, too imprecise material, or having similar titles, we decided upon 119, generally empirical, sources. Future research should investigate whether Industry 4.0-based manufacturing technologies can ensure the sustainability of big data-driven production systems by use of Internet of Things sensing networks and deep learning-assisted smart process planning.

The article aims to present a thorough research on the perceptions and opinions of the Romanian managers of small and medium enterprises in applied biotechnology on the importance of intellectual capital and the application of knowledge management principles to create and maintain competitive advantages. At the basis of the development of bio-economics, there is a successful implementation not only of top biotechnologies but also of new economic models that engage the economic agents in complex exchanges. Biotechnology companies are a true "engine" that helps the development of bio-economy basic mechanisms, optimization of their work having longterm repercussions. Companies that develop a sustainable knowledge management system that they integrate into their marketing strategy have the most prominent position on the market and gain multiple competitive advantages. The research, based on the qualitative research methodology in the form of an in-depth interview, highlights that the strategic decision regarding the implementation of a knowledge management system and the intelligent use of intellectual capital resources are correlated with variables such as: the managers' level of education in the field, corelating managers' activity to organizational culture. Knowledge, for new business models, is a good asset that can be capitalized; from this perspective, the implications at the level of marketing strategies are in the same time diverse and complex. Biotechnology SMEs will adapt to requirements by developing competitive advantages as a result of establishing relationships and developing exchanges within strategic alliances and less according to a classical model based on attributes that aims positioning of products or services above those of the competition.

The objective of this systematic review was to analyze the recently published literature on the Internet of Robotic Things (IoRT) and integrate the insights it articulates on big data management algorithms, deep learning-based object detection technologies, and geospatial simulation and sensor fusion tools. The research problems were whether computer vision techniques, geospatial data mining, simulation-based digital twins, and real-time monitoring technology optimize remote sensing robots. Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines were leveraged by a Shiny app to obtain the flow diagram comprising evidence-based collected and managed data (the search results and screening procedures). Throughout January and July 2022, a quantitative literature review of ProQuest, Scopus, and the Web of Science databases was performed, with search terms comprising “Internet of Robotic Things” + “big data management algorithms”, “deep learning-based object detection technologies”, and “geospatial simulation and sensor fusion tools”. As the analyzed research was published between 2017 and 2022, only 379 sources fulfilled the eligibility standards. A total of 105, chiefly empirical, sources have been selected after removing full-text papers that were out of scope, did not have sufficient details, or had limited rigor For screening and quality evaluation so as to attain sound outcomes and correlations, we deployed AMSTAR (Assessing the Methodological Quality of Systematic Reviews), AXIS (Appraisal tool for Cross-Sectional Studies), MMAT (Mixed Methods Appraisal Tool), and ROBIS (to assess bias risk in systematic reviews). Dimensions was leveraged as regards initial bibliometric mapping (data visualization) and VOSviewer was harnessed in terms of layout algorithms.