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Internet has changed radically in the way people interact in the virtual world, in their careers or social relationships. IoT technology has added a new vision to this process by enabling connections between smart objects and humans, and also between smart objects themselves, which leads to anything, anytime, anywhere, and any media communications. IoT allows objects to physically see, hear, think, and perform tasks by making them talk to each other, share information and coordinate decisions. To enable the vision of IoT, it utilizes technologies such as ubiquitous computing, context awareness, RFID, WSN, embedded devices, CPS, communication technologies, and internet protocols. IoT is considered to be the future internet, which is significantly different from the Internet we use today. The purpose of this paper is to provide up-to-date literature on trends of IoT research which is driven by the need for convergence of several interdisciplinary technologies and new applications.

A comprehensive IoT literature review has been performed in this paper as a survey. The survey starts by providing an overview of IoT concepts, visions and evolutions. IoT architectures are also explored. Then, the most important components of IoT are discussed including a thorough discussion of IoT operating systems such as Tiny OS, Contiki OS, FreeRTOS, and RIOT. A review of IoT applications is also presented in this paper and finally, IoT challenges that can be recently encountered by researchers are introduced.

Studies of IoT literature and projects show the disproportionate importance of technology in IoT projects, which are often driven by technological interventions rather than innovation in the business model. There are a number of serious concerns about the dangers of IoT growth, particularly in the areas of privacy and security; hence, industry and government began addressing these concerns. At the end, what makes IoT exciting is that we do not yet know the exact use cases which would have the ability to significantly influence our lives.

This survey provides a comprehensive literature review on IoT techniques, operating systems and trends.

More and more, the leaders of business functions are turning for competitive insights to the massive data they can now capture. But to date, human resources departments have lagged behind the efforts of marketing, IT, CRM and other functions. The purpose of this article is to show how executives can start using data to measure and improve HR's contributions to business performance.

The article identifies six analytical tools that HR can use to connect HR efforts to business performance. Survey results underscore the value of an analytical approach while revealing that many HR departments are heavily focused on internal measures rather than business outcomes. Each analytical tool is exemplified through case studies. A model is presented to suggest how executives can get started by focusing on five key areas.

Leading companies are using six analytical tools to improve the connection between HR investments and business returns: employee databases; segmentation of talent; targeted investments; customization of the employee value proposition; long‐term workforce planning; and talent supply chains.

As the case studies reveal, the tools identified here can help HR leaders actively shape their organization's future – managing talent and directing programs toward the long‐term needs of the business. Survey data shows that most companies increasingly seek to use analytics for long‐term advantage, and the model presented here can help HR executives take the first critical steps.

This chapter of the book seeks to use famous mathematical functions (statistical distribution functions) in evaluating and analyzing supply chain network data related to supply chain management (SCM) elements in organizations. In other words, the main purpose of this chapter is to find the best-fitted statistical distribution functions for SCM data. Explaining how to best fit the statistical distribution function along with the explanation of all possible aspects of a function for selected components of SCM from this chapter will make a significant attraction for production and services experts who will lead their organization to the path of competitive excellence. The main core of the chapter is the reliability values related to the reliability function calculated by the relevant chart and extracting other information based on other aspects of statistical distribution functions such as probability density, cumulative distribution, and failure function. This chapter of the book will turn readers into professional users of statistical distribution functions in mathematics for analyzing supply chain element data.

The purpose of this study is to explore the potential for the development of a country logistics performance assessment approach based upon textual big data analytics.

The study employs design science principles. Data were collected using the Global Perspectives text corpus that describes the logistics systems of 20 countries from 2006–2014. The extracted texts were processed and analysed using text analytic techniques, and domain experts were employed for training and developing the approach.

The developed approach is able to generate results in the form of logistics performance assessments. It contributes towards the development of more informed weights of the different country logistics performance categories. That said, a larger text corpus and iterative classifier training is required to produce a more robust approach for benchmarking and ranking.

When successfully developed and implemented, the developed approach can be used by managers and government bodies, such as the World Bank and its stakeholders, to complement the Logistics Performance Index (LPI).

A new and unconventional approach for logistics system performance assessment is explored. A new potential for textual big data analytic applications in supply chain management is demonstrated. A contribution to performance management in operations and supply chain management is made by demonstrating how domain-specific text corpora can be transformed into an important source of performance information.

This paper aims to examine factors influencing the practices of big data analytics applications by commercial banks operating in Jordan and their bank performance.

A conceptual framework was developed in this regard based on a comprehensive literature review and the Technology–Environment–Organization (TOE) model. A quantitative approach was used, and the data was collected from 235 commercial banks’ senior and middle managers (IT, financial and marketers) using both online and paper-based questionnaires.

The results showed that the extent of the practices of big data analytics applications by commercial banks operating in Jordan is considered to be moderate (i.e. 60%). The results indicated that 61% of the variation on the practices of big data analytics applications by commercial banks could be predicated by TOE model. The organizational factors were found the most important predictors. The results also provide empirical evidence that the extent of practices of big data analytics applications has a positive influence on the bank performance. In the final section, research implications and future directions are presented.

This paper contributes to theory by filling a gap in the literature regarding the extent of the practices of big data analytics applications by commercial banks operating in developing countries, such as Jordan. It empirically examines the impact of the practices of big data analytics applications on bank performance.

In today’s dynamic and changing environment, companies have a strong need to create or sustain their competitive advantages. In order to be competitive, companies need to be responsive and closer to the customers, and deliver value‐added products and services as quickly as possible. Companies also need to be able to support organizational information needs faster and better than their competitors. These goals can be realized by applying two emerging information technologies: enterprise resource planning (ERP) supporting business process integration; and data warehousing supporting data integration. Companies with the further integration of ERP and data warehousing will have great advantages in the competitive environment. Two cases have been studied and presented to illustrate its values.

To be more effective, artificial intelligence (AI) requires a broad overall view of the design and transformation of enterprise architecture and capabilities. Maturity models (MMs) are the recognized tools to identify strengths and weaknesses of certain domains of an organization. They consist of multiple, archetypal levels of maturity of a certain domain and can be used for organizational assessment and development. In the case of AI, quite a few numbers of MMs have been proposed. Generally, the links between AI technology, AI usage and organizational performance stay unclear. To address these gaps, this paper aims to introduce the complete details of the AI maturity model (AIMM) for AI-driven platform companies. The associated AI-Driven Platform Enterprise Maturity framework proposed here can help to achieve most of the AI-driven platform companies' objectives.

Qualitative research is performed in two stages. In the first stage, a review of the existing literature is performed to identify the types, barriers, drivers, challenges and opportunities of MMs in AI, Advanced Analytics and Big Data domains. In the second stage, a research framework is proposed to align company value chain with AI technologies and levels of the platform enterprise maturity.

The paper proposes a new five level AI-Driven Platform Enterprise Maturity framework by constructing a formal organizational value chain taxonomy model that explains a vast group of MM phenomena related with the AI-Driven Platform Enterprises. In addition, this study proposes a clear and precise description and structuring of the information in the multidimensional Platform, AI, Advanced Analytics and Big Data domains. The AI-Driven Platform Enterprise Maturity framework assists in identification, creation, assessment and disclosure research of AI-driven platform business organizations.

This research is focused on the basic dimensions of AI value chain. The full reference model of AI consists of much more concepts. In the last few years, AI has achieved a notable drive that, if connected appropriately, may deliver the best of expectations over many application sectors across the field. For this to occur shortly in machine learning, especially in deep neural networks, the entire community stands in front of the barrier of explainability. Paradigms underlying this problem fall within the so-called eXplainable AI (XAI) field, which is widely acknowledged as a crucial feature for the practical deployment of AI models in industry. Our prospects lead toward the concept of a methodology for the large-scale implementation of AI methods in platform organizations with fairness, model explainability and accountability at its core.

AI-driven platform enterprise maturity framework can be used for better communicate to clients the value of AI capabilities through the lens of changing human-machine interactions and in the context of legal, ethical and societal norms.

The authors discuss AI in the enterprise platform stack including talent platform, human capital management and recruiting.

The AI value chain and AI-Driven Platform Enterprise Maturity framework are original and represent an effective tools for assessing AI-driven platform enterprises.

Big data has posed problems for businesses, the Information Technology (IT) sector and the science community. The problems posed by big data can be effectively addressed using cloud computing and associated distributed computing technology. Cloud computing and big data are two significant past-year problems that allow high-efficiency and competitive computing tools to be delivered as IT services. The paper aims to examine the role of the cloud as a tool for managing big data in various aspects to help businesses.

This paper delivers solutions in the cloud for storing, compressing, analyzing and processing big data. Hence, articles were divided into four categories: articles on big data storage, articles on big data processing, articles on analyzing and finally, articles on data compression in cloud computing. This article is based on a systematic literature review. Also, it is based on a review of 19 published papers on big data.

From the results, it can be inferred that cloud computing technology has features that can be useful for big data management. Challenging issues are raised in each section. For example, in storing big data, privacy and security issues are challenging.

There were limitations to this systematic review. The first limitation is that only English articles were reviewed. Also, articles that matched the keywords were used. Finally, in this review, authoritative articles were reviewed, and slides and tutorials were avoided.

The research presents new insight into the business value of cloud computing in interfirm collaborations.

Previous research has often examined other aspects of big data in the cloud. This article takes a new approach to the subject. It allows big data researchers to comprehend the various aspects of big data management in the cloud. In addition, setting an agenda for future research saves time and effort for readers searching for topics within big data.

The purpose of this paper is to describe the main ways in which large amounts of information have been integrated to provide new measures of food consumption and agricultural production, and new methods for gathering and analyzing internet-based data.

This study reviews some of the recent developments and applications of big data, which is becoming increasingly popular in agricultural economics research. In particular, this study focuses on applications of new types of data such as text and graphics in consumers' online reviews emerging from e-commerce transactions and Normalized Difference Vegetation Index (NDVI) data as well as other producer data that are gaining popularity in precision agriculture. This study then reviews data gathering techniques such as web scraping and data analytics tools such as textual analysis and machine learning.

This study provides a comprehensive review of applications of big data in agricultural economics and discusses some potential future uses of big data.

This study documents some new types of data that are being utilized in agricultural economics, sources and methods to gather and store such data, existing applications of these new types of data and techniques to analyze these new data.

The purpose of this paper is to make a lean assessment of a chemical analyst training laboratory in a higher education institution and identify the main types of waste on a daily basis and understand the lean maturity of the laboratory and establish priority areas of intervention to make the laboratory leanest.

A single descriptive case study methodology was used to carry out the lean laboratory evaluation. The lean manufacturing waste terminology was adapted to a lean analytical laboratory environment, and a lean waste assessment step-by-step procedure was developed to reach the study goal.

Three types of waste (i.e. transport, waiting and defects) were the main contributors of the problem. The Pareto analysis results showed that 37.5% of the different types of waste contributed to almost 51.4% of the problems. The case study allowed on diagnosing wastes, understanding the lean maturity in a teaching laboratory setting and priority areas of intervention

Some data collection methods were used, and tools were developed to answer the research questions. A waste measurement instrument was created to evaluate lean waste in a chemical analytical laboratory, and a lean classification scheme was built to understand the lean maturity of the laboratory. The lessons learnt of the lean assessment in a teaching laboratory and the developed tools will be helpful for future research and for practitioners in a teaching chemical analytical laboratory setting.

The number of lean assessment studies in teaching laboratories is not very significant, and this work contributes to overcome this gap illustrating the lean waste assessment foundation with a step-by-step procedure and tools used in a teaching laboratory to perform a lean assessment and identify opportunities for improvement. A generic roadmap to lean laboratory waste assessment and continuous improvement is proposed with the key elements to take into consideration.