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The purpose of this study is to develop a synthesized conceptual framework for artificial intelligence (AI) adoption in the field of business-to-business (B2B) marketing.

A conceptual development approach has been adopted, based on a content analysis of 59 papers in peer-reviewed academic journals, to identify drivers, barriers, practices and consequences of AI adoption in B2B marketing. Based on these analyses and findings, a conceptual model is developed.

This paper identifies the following two key drivers of AI adoption: the shortcomings of current marketing activities and the external pressure imposed by informatization. Seven outcomes are identified, namely, efficiency improvements, accuracy improvements, better decision-making, customer relationship improvements, sales increases, cost reductions and risk reductions. Based on information processing theory and organizational learning theory (OLT), an integrated conceptual framework is developed to explain the relationship between each construct of AI adoption in B2B marketing.

This study is the first conceptual paper that synthesizes drivers, barriers and outcomes of AI adoption in B2B marketing. The conceptual model derived from the combination of information processing theory and OLT provides a comprehensive framework for future work and opens avenues of research on this topic. This paper contributes to both AI literature and B2B literature.

In today's fast developing era, the volume of data is increasing day by day. The traditional methods are lagging for efficiently managing the huge amount of data. The adoption of machine learning techniques helps in efficient management of data and draws relevant patterns from that data. The main aim of this research paper is to provide brief information about the proposed adoption of machine learning techniques in different sectors of manufacturing supply chain.

This research paper has done rigorous systematic literature review of adoption of machine learning techniques in manufacturing supply chain from year 2015 to 2023. Out of 511 papers, 74 papers are shortlisted for detailed analysis.

The papers are subcategorised into 8 sections which helps in scrutinizing the work done in manufacturing supply chain. This paper helps in finding out the contribution of application of machine learning techniques in manufacturing field mostly in automotive sector.

The research is limited to papers published from year 2015 to year 2023. The limitation of the current research that book chapters, unpublished work, white papers and conference papers are not considered for study. Only English language articles and review papers are studied in brief. This study helps in adoption of machine learning techniques in manufacturing supply chain.

This study is one of the few studies which investigate machine learning techniques in manufacturing sector and supply chain through systematic literature survey.

1. A comprehensive understanding of Machine Learning techniques is presented.

2. The state of art of adoption of Machine Learning techniques are investigated.

3. The methodology of (SLR) is proposed.

4. An innovative study of Machine Learning techniques in manufacturing supply chain.

A comprehensive understanding of Machine Learning techniques is presented.

The state of art of adoption of Machine Learning techniques are investigated.

The methodology of (SLR) is proposed.

An innovative study of Machine Learning techniques in manufacturing supply chain.

Smart card-based E-payment systems are receiving increasing attention as the number of implementations is witnessed on the rise globally. Understanding of user adoption behavior of E-payment systems that employ smart card technology becomes a research area that is of particular value and interest to both IS researchers and professionals. However, research interest focuses mostly on why a smart card-based E-payment system results in a failure or how the system could have grown into a success. This signals the fact that researchers have not had much opportunity to critically review a smart card-based E-payment system that has gained wide support and overcome the hurdle of critical mass adoption. The Octopus in Hong Kong has provided a rare opportunity for investigating smart card-based E-payment system because of its unprecedented success. This research seeks to thoroughly analyze the Octopus from technology adoption behavior perspectives.

Cultural impacts on adoption behavior are one of the key areas that this research posits to investigate. Since the present research is conducted in Hong Kong where a majority of population is Chinese ethnicity and yet is westernized in a number of aspects, assuming that users in Hong Kong are characterized by eastern or western culture is less useful. Explicit cultural characteristics at individual level are tapped into here instead of applying generalization of cultural beliefs to users to more accurately reflect cultural bias. In this vein, the technology acceptance model (TAM) is adapted, extended, and tested for its applicability cross-culturally in Hong Kong on the Octopus. Four cultural dimensions developed by Hofstede are included in this study, namely uncertainty avoidance, masculinity, individualism, and Confucian Dynamism (long-term orientation), to explore their influence on usage behavior through the mediation of perceived usefulness.

TAM is also integrated with the innovation diffusion theory (IDT) to borrow two constructs in relation to innovative characteristics, namely relative advantage and compatibility, in order to enhance the explanatory power of the proposed research model. Besides, the normative accountability of the research model is strengthened by embracing two social influences, namely subjective norm and image. As the last antecedent to perceived usefulness, prior experience serves to bring in the time variation factor to allow level of prior experience to exert both direct and moderating effects on perceived usefulness.

The resulting research model is analyzed by partial least squares (PLS)-based Structural Equation Modeling (SEM) approach. The research findings reveal that all cultural dimensions demonstrate direct effect on perceived usefulness though the influence of uncertainty avoidance is found marginally significant. Other constructs on innovative characteristics and social influences are validated to be significant as hypothesized. Prior experience does indeed significantly moderate the two influences that perceived usefulness receives from relative advantage and compatibility, respectively. The research model has demonstrated convincing explanatory power and so may be employed for further studies in other contexts. In particular, cultural effects play a key role in contributing to the uniqueness of the model, enabling it to be an effective tool to help critically understand increasingly internationalized IS system development and implementation efforts. This research also suggests several practical implications in view of the findings that could better inform managerial decisions for designing, implementing, or promoting smart card-based E-payment system.

Several factors influence OSC adoption, but extant literature did not articulate the dominant barriers or drivers influencing adoption. Therefore, this research has not only ventured into analyzing the core influencing factors but has also employed one of the best-known predictive means, Machine Learning, to identify the most influencing OSC adoption factors.

The research approach is deductive in nature, focusing on finding out the most critical factors through literature review and reinforcing — the factors through a 5- point Likert scale survey questionnaire. The responses received were tested for reliability before being run through Machine Learning algorithms to determine the most influencing OSC factors within the Nigerian Construction Industry (NCI).

The research outcome identifies seven (7) best-performing algorithms for predicting OSC adoption: Decision Tree, Random Forest, K-Nearest Neighbour, Extra-Trees, AdaBoost, Support Vector Machine and Artificial Neural Network. It also reported finance, awareness, use of Building Information Modeling (BIM) and belief in OSC as the main influencing factors.

Data were primarily collected among the NCI professionals/workers and the whole exercise was Nigeria region-based. The research outcome, however, provides a foundation for OSC adoption potential within Nigeria, Africa and beyond.

The research concluded that with detailed attention paid to the identified factors, OSC usage could find its footing in Nigeria and, consequently, Africa. The models can also serve as a template for other regions where OSC adoption is being considered.

The research establishes the most effective algorithms for the prediction of OSC adoption possibilities as well as critical influencing factors to successfully adopting OSC within the NCI as a means to surmount its housing shortage.

In the last four years, since Volume I of this Bibliography first appeared, there has been an explosion of literature in all the main functional areas of business. This wealth of material poses problems for the researcher in management studies — and, of course, for the librarian: uncovering what has been written in any one area is not an easy task. This volume aims to help the librarian and the researcher overcome some of the immediate problems of identification of material. It is an annotated bibliography of management, drawing on the wide variety of literature produced by MCB University Press. Over the last four years, MCB University Press has produced an extensive range of books and serial publications covering most of the established and many of the developing areas of management. This volume, in conjunction with Volume I, provides a guide to all the material published so far.

This paper aims to inform policymakers about key artificial intelligence (AI) technologies, risks and trends in national AI strategies. It suggests a framework of social governance to ensure emergence of safe and beneficial AI.

The paper is based on approximately 100 interviews with researchers, executives of traditional companies and startups and policymakers in seven countries. The interviews were carried out in January-August 2017.

Policymakers still need to develop an informed, scientifically grounded and forward-looking view on what societies and businesses might expect from AI. There is lack of transparency on what key AI risks are and what might be regulatory approaches to handle them. There is no collaborative framework in place involving all important actors to decide on AI technology design principles and governance. Today's technology decisions will have long-term consequences on lives of billions of people and competitiveness of millions of businesses.

The research did not include a lot of insights from the emerging markets.

Policymakers will understand the scope of most important AI concepts, risks and national strategies.

AI is progressing at a very fast rate, changing industries, businesses and approaches how companies learn, generate business insights, design products and communicate with their employees and customers. It has a big societal impact, as – if not designed with care – it can scale human bias, increase cybersecurity risk and lead to negative shifts in employment. Like no other invention, it can tighten control by the few over the many, spread false information and propaganda and therewith shape the perception of people, communities and enterprises.

This paper is a compendium on the most important concepts of AI, bringing clarity into discussions around AI risks and the ways to mitigate them. The breadth of topics is valuable to policymakers, students, practitioners, general executives and board directors alike.

The ideas expressed in this work are based on those put into practice at the Okuma Corporation of Japan, one of the world′s leading machine tool manufacturers. In common with many other large organizations, Okuma Corporation has to meet the new challenges posed by globalization, keener domestic and international competition, shorter business cycles and an increasingly volatile environment. Intelligent corporate strategy (ICS), as practised at Okuma, is a unified theory of strategic corporate management based on five levels of win‐win relationships for profit/market share, namely: ,1. Loyalty from customers (value for money) – right focus., 2. Commitment from workers (meeting hierarchy of needs) – right attitude., 3. Co‐operation from suppliers (expanding and reliable business) – right connections., 4. Co‐operation from distributors (expanding and reliable business) – right channels., 5. Respect from competitors (setting standards for business excellence) – right strategies. The aim is to create values for all stakeholders. This holistic people‐oriented approach recognizes that, although the world is increasingly driven by high technology, it continues to be influenced and managed by people (customers, workers, suppliers, distributors, competitors). The philosophical core of ICS is action learning and teamwork based on principle‐centred relationships of sincerity, trust and integrity. In the real world, these are the roots of success in relationships and in the bottom‐line results of business. ICS is, in essence, relationship management for synergy. It is based on the premiss that domestic and international commerce is a positive sum game: in the long run everyone wins. Finally, ICS is a paradigm for manufacturing companies coping with change and uncertainty in their search for profit/market share. Time‐honoured values give definition to corporate character; circumstances change, values remain. Poor business operations generally result from human frailty. ICS is predicated on the belief that the quality of human relationships determines the bottom‐line results. ICS attempts to make manifest and explicit the intangible psychological factors for value‐added partnerships. ICS is a dynamic, living, and heuristic‐learning model. There is intelligence in the corporate strategy because it applies commonsense, wisdom, creative systems thinking and synergy to ensure longevity in its corporate life for sustainable competitive advantage.

The concept and practice of e-services has become essential in business transactions. Yet there are still many organizations that have not developed e-services optimally. This is especially relevant in the context of Indonesian Airline companies. Therefore, many airline customers in Indonesia are still in doubt about it, or even do not use it. To fill this gap, this study attempts to develop a model for e-services adoption and empirically examines the factors influencing the airlines customers in Indonesia in using e-services offered by the Indonesian airline companies. Taking six Indonesian airline companies as a case example, the study investigated the antecedents of e-services usage of Indonesian airlines. This study further examined the impacts of motivation on customers in using e-services in the Indonesian context. Another important aim of this study was to investigate how ages, experiences and geographical areas moderate effects of e-services usage.

The study adopts a positivist research paradigm with a two-phase sequential mixed method design involving qualitative and quantitative approaches. An initial research model was first developed based on an extensive literature review, by combining acceptance and use of information technology theories, expectancy theory and the inter-organizational system motivation models. A qualitative field study via semi-structured interviews was then conducted to explore the present state among 15 respondents. The results of the interviews were analysed using content analysis yielding the final model of e-services usage. Eighteen antecedent factors hypotheses and three moderating factors hypotheses and 52-item questionnaire were developed. A focus group discussion of five respondents and a pilot study of 59 respondents resulted in final version of the questionnaire.

In the second phase, the main survey was conducted nationally to collect the research data among Indonesian airline customers who had already used Indonesian airline e-services. A total of 819 valid questionnaires were obtained. The data was then analysed using a partial least square (PLS) based structural equation modelling (SEM) technique to produce the contributions of links in the e-services model (22% of all the variances in e-services usage, 37.8% in intention to use, 46.6% in motivation, 39.2% in outcome expectancy, and 37.7% in effort expectancy). Meanwhile, path coefficients and t-values demonstrated various different influences of antecedent factors towards e-services usage. Additionally, a multi-group analysis based on PLS is employed with mixed results. In the final findings, 14 hypotheses were supported and 7 hypotheses were not supported.

The major findings of this study have confirmed that motivation has the strongest contribution in e-services usage. In addition, motivation affects e-services usage both directly and indirectly through intention-to-use. This study provides contributions to the existing knowledge of e-services models, and practical applications of IT usage. Most importantly, an understanding of antecedents of e-services adoption will provide guidelines for stakeholders in developing better e-services and strategies in order to promote and encourage more customers to use e-services. Finally, the accomplishment of this study can be expanded through possible adaptations in other industries and other geographical contexts.

Machine learning is beginning to transform the way businesses organize their operations and benefit from technology investments.

To learn more about how far along organizations are in deploying intelligent automation and in developing plans and strategies for its adoption, the IBM Institute for Business Value, in collaboration with Oxford Economics surveyed and interviewed 550 technology and operations executives.

The primary purpose of intelligent automation is to augment employees’ skills, experience and expertise, extending the human mind in ways that allow for higher productivity, creative problem-solving and more engaging jobs for employees.

Automation is not a plug-and-play solution: companies cannot just buy the technology, flip the switch and watch robots run the business without any human intervention.

This recent survey of operations executive with specific knowledge of their companies plans provides insights into best practice. Executives believe that layering new technologies on top of old business processes is apt to be less productive ? and less cost-effective ? than rethinking processes to make the most of intelligent automation. Executives must optimize workflows for automation; this means envisioning the end result, enabling it through logical steps and prototyping the process ? then repairing as necessary before scaling.

Although businesses continue to take up artificial intelligence (AI), concerns remain that companies are not realising the full value of their investments. The study aims to provide insights into how AI creates business value by investigating the mediating role of Business Process Management (BPM) capabilities.

The integrative model of IT Business Value was contextualised, and structural equation modelling was applied to validate the proposed serial multiple mediation model using a sample of 448 organisations based in the EU.

The results validate the proposed serial multiple mediation model according to which AI adoption increases organisational performance through decision-making and business process performance. Process automation, organisational learning and process innovation are significant complementary partial mediators, thereby shedding light on how AI creates business value.

In pursuing a complex nomological framework, multiple perspectives on realising business value from AI investments were incorporated. Several moderators presenting complementary organisational resources (e.g. culture, digital maturity, BPM maturity) could be included to identify behaviour in more complex relationships. The ethical and moral issues surrounding AI and its use could also be examined.

The provided insights can help guide organisations towards the most promising AI activities of process automation with AI-enabled decision-making, organisational learning and process innovation to yield business value.

While previous research assumed a moderated relationship, this study extends the growing literature on AI business value by empirically investigating a comprehensive nomological network that links AI adoption to organisational performance in a BPM setting.