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The largest business unit is no longer the multinational corporation but rather, cooperating networks of globally connected organizations and firms. The principal focus of this chapter is to identify trends which have come to a head in the last two decades in the world economy and have fostered interorganizational cooperation on an unprecedented scale. The technological, political, spatial and cultural complexities of global operations are becoming simply too great in many areas to be encompassed by a single company or agency. It is impossible to build a technologically complex global civilization without interorganizational cooperation, any more than a multicellular organism could survive without cooperation between its distinct but specialized constituents. The chapter reviews the evolutionary basis for cooperation, describes recent economic forces that have shaped interorganizational cooperation and describes why planetary economic cooperation is our “manifest destiny.”

Internet of Things (IoT) and artificial intelligence are two leading technologies that bought revolution to each and every field of humans using in daily life by making everything smarter than ever. IoT leads to a network of things which creates a self-configuring network. Improving farm productivity is essential to meet the rapidly growing demand for food. In this chapter, the authors have introduced a smart greenhouse by integration of two leading technologies in the market (i.e., Machine Learning and IoT). In proposed model, several sensors are used for data collection and managing the environment of greenhouse. The idea is to propose an IoT and Machine Learning based smart nursery that helps in healthy growing and monitoring of the seed. The structure will be a dome-like structure for observation and isolation of an egg with various sensors like pressure, humidity, temperature, light, moisture, conductivity, air quality, etc. to monitor the nursery internal environment and maintain the control and flow of water and other minerals inside the nursery. The nursery will have a solar panel from which it stores the electricity generated from the sun, a small fan to control the flow of air and pressure. A camera will also be equipped inside the nursery that will use computer vision technology to monitor the health of the plant and will be trained on the past data to notify the user if the plant is diseased or need attention.

Purpose – This chapter proposes three main objectives in relation to understanding customer involvement in business networks. First, to identify important aspects of the network structure and environment and how the actions of the customer and other network participants create and maintain these. Second, to identify and explore the mechanisms and processes of resource integration in the network. Third, to identify the capabilities and competencies that customers bring to the network, and to understand how these are enhanced and developed.

Methodology/approach – Conceptual.

Research implications – We recognize that aspects of the resources themselves are important and that the characteristics of the resource and the way in which partners align them were key components of resource analysis.

Practical implications – We note that the interaction of different operant and operand resource combinations opens new doors to customer knowledgeability and involvement, where power over either authoritative or allocative resources in itself will not guarantee value creation.

Social implications – We support the call for the development of more sociologically enriched and complex models of interagent resource exchange. In particular, we would advise the need for a better understanding of how different network structures and environments are created and maintained through domination, legitimation, and signification processes.

Originality/value of chapter – This chapter addresses the gap in our understanding of how customer involvement in business-to-business networks may influence learning, value cocreation, and innovation. This chapter makes an important contribution to research in the field in that it investigates how the inclusion of the customer in business networks alters current assumptions and practices.

As the world economies have become more integrated and with the global economy subsequently growing, there is increasing concern regarding how such trends will affect the developing and developed nations’ trade, trade in services, gross domestic product (GDP) growth, and climate change. In fact, the relationship between globalization and the environment has become quite contentious in policy circles. In part in response to these controversies, a burgeoning amount of academic attention has emerged that examines the globalization linkage between trade and economic growth as an after effect (positive and negative). Although there have been advances in the thinking about these relationships, significant challenges still persist. In light of the above, this chapter talks about the evolution of globalization, prevailing benefits and also caters to the views of some famous economists like Stiglitz, Simon Kuznets. The crucial aspects include the review of globalization on the basis of certain parameters like Trade (as % of GDP), Trade in services (as % of GDP), per capita CO₂ emissions and per capita GDP that have impacted the trends of both developed and developing nations. It arrives at the position that many current and proposed national and multilateral environmental policies are in a possible conflict with current and proposed trade and investment rules. When climate policy and global trade rules are combined, the nature of their linkage is often a function of both domestic and international politics. This also implies that, despite the worldwide awareness of climate change, the address of climate change in trade will become increasingly significant for reducing carbon footprints.

In the last few decades, there has been growing interest in forecasting with computer intelligence, and both fuzzy time series (FTS) and artificial neural networks (ANNs) have gained particular popularity, among others. Rather than the conventional methods (e.g., econometrics), FTS and ANN are usually thought to be immune to fundamental concepts such as stationarity, theoretical causality, post-sample control, among others. On the other hand, a number of studies significantly indicated that these fundamental controls are required in terms of the theory of forecasting, and even application of such essential procedures substantially improves the forecasting accuracy. The aim of this paper is to fill the existing gap on modeling and forecasting in the FTS and ANN methods and figure out the fundamental concepts in a comprehensive work through merits and common failures in the literature. In addition to these merits, this paper may also be a guideline for eliminating unethical empirical settings in the forecasting studies.