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The purpose of this paper is to conduct an exploratory study of the branding of business‐to‐business (B2B) services, specifically examining the commodity‐like logistics services industry.

The paper is of a multiple‐methods research design.

Managers should first strive to develop compelling and differentiated value propositions associated with their B2B service brands. They should then invest in communicating their brands' value to internal and external audiences. Finally, they should commit resources to ensure consistent and favorable customer experiences with the brand. These three steps influence the strength of the brand, which comprises brand awareness and brand meaning.

B2B service firms in commodity‐like industries such as the logistics service industry cannot rely on differences in product attributes to develop brand meaning. Rather, they should focus on developing distinctive customer experiences with the brand by encouraging meaningful employee‐customer interactions. Such differentiated value propositions based on superior customer experiences build brand awareness and enhance the brand's meaning with current and prospective customers, thereby increasing brand equity.

Knowledge of branding practices in B2B service contexts is limited. This research addresses this knowledge gap.

This paper aims to use different machine learning (ML) algorithms for the prediction of inverse kinematic solutions in parallel manipulators (PMs) to overcome the computational difficulties and approximations involved with the analytical methods. The results obtained from the ML algorithms and the Denavit–Hartenberg (DH) approach are compared with the experimental results to evaluate their performances. The study is performed on a novel 6-degree of freedom (DoF) PM that offers precise motions with a large workspace for the end effector.

The kinematic model for the proposed 3-PPSS PM is obtained using the modified DH approach and its inverse kinematic solutions are determined using the Levenberg–Marquardt algorithm. Various prediction algorithms such as the multiple linear regression, multi-variate polynomial regression, support vector, decision tree, random forest regression and multi-layer perceptron networks are applied to predict the inverse kinematic solutions for the manipulator. The data set required to train the network is generated experimentally by recording the poses of the end effector for different instantaneous positions of the slider using the concept of ArUco markers.

This paper fully demonstrates the possibility to use artificial intelligence for the prediction of inverse kinematic solutions especially for complex geometries.

As the analytical models derived from the geometrical method, Screw theory or numerical techniques involve approximations and needs more computational power, it is not advisable for real-time control of the manipulator. In addition, the data set obtained from the derived inverse kinematic equations to train the network may lead to inaccuracies in the predicted results. This error may generate significant deviations in the end-effector position from the desired position. The present work attempts to resolve this issue by proposing a camera-based approach that uses ArUco library and ML algorithms to create the data set experimentally and predict the inverse kinematic solutions accurately.

Forecasts of commodity prices are vital issues to market participants and policy makers. Those of corn are of no exception, considering its strategic importance. In the present study, the authors assess the forecast problem for the weekly wholesale price index of yellow corn in China during January 1, 2010–January 10, 2020 period.

The authors employ the nonlinear auto-regressive neural network as the forecast tool and evaluate forecast performance of different model settings over algorithms, delays, hidden neurons and data splitting ratios in arriving at the final model.

The final model is relatively simple and leads to accurate and stable results. Particularly, it generates relative root mean square errors of 1.05%, 1.08% and 1.03% for training, validation and testing, respectively.

Through the analysis, the study shows usefulness of the neural network technique for commodity price forecasts. The results might serve as technical forecasts on a standalone basis or be combined with other fundamental forecasts for perspectives of price trends and corresponding policy analysis.

Although managing global change is one of the key competencies demanded of global leaders, it is one of the most under-researched topics in the field (Lane, Spector, Osland, & Taylor, 2014). This chapter shares findings from a recent qualitative study that examined how global business leaders navigate complex global changes. Data were collected from 23 global business executives working for 20 unique global enterprises, in 12 different functions, through a pre-interview participant qualifying profile, an in-depth semi-structured interview, and follow-up verification. Findings reveal that global business executives are contextual leaders who juggle both global task and global relationship complexities. The paradox is the process they employ to navigate continuous change, enabled by sensemaking. Finally, as agile learners, they prove that the global leadership capabilities required to navigate paradox can be learned.

To be successful in the new global environment, twenty‐first century leaders must increase their ability to function in seven key roles, namely, as a systems thinker, change agent, innovator, servant, polychronic co‐ordinator, teacher‐mentor and visionary. Action learning has quickly emerged as one of the most effective and powerful tools in developing the necessary competencies and experiences to carry out these roles. In this article, the author describes how the elements of action learning (i.e. real problems, fellow leaders in the action learning team, a reflective inquiry process, commitment to action, and focusing on learning) contribute to the building of each of these critical leadership skills.

The purpose of this paper is three-fold: to review the categories explaining mainly optimization algorithms (techniques) in that needed to improve the generalization performance and learning speed of the Feedforward Neural Network (FNN); to discover the change in research trends by analyzing all six categories (i.e. gradient learning algorithms for network training, gradient free learning algorithms, optimization algorithms for learning rate, bias and variance (underfitting and overfitting) minimization algorithms, constructive topology neural networks, metaheuristic search algorithms) collectively; and recommend new research directions for researchers and facilitate users to understand algorithms real-world applications in solving complex management, engineering and health sciences problems.

The FNN has gained much attention from researchers to make a more informed decision in the last few decades. The literature survey is focused on the learning algorithms and the optimization techniques proposed in the last three decades. This paper (Part II) is an extension of Part I. For the sake of simplicity, the paper entitled “Machine learning facilitated business intelligence (Part I): Neural networks learning algorithms and applications” is referred to as Part I. To make the study consistent with Part I, the approach and survey methodology in this paper are kept similar to those in Part I.

Combining the work performed in Part I, the authors studied a total of 80 articles through popular keywords searching. The FNN learning algorithms and optimization techniques identified in the selected literature are classified into six categories based on their problem identification, mathematical model, technical reasoning and proposed solution. Previously, in Part I, the two categories focusing on the learning algorithms (i.e. gradient learning algorithms for network training, gradient free learning algorithms) are reviewed with their real-world applications in management, engineering, and health sciences. Therefore, in the current paper, Part II, the remaining four categories, exploring optimization techniques (i.e. optimization algorithms for learning rate, bias and variance (underfitting and overfitting) minimization algorithms, constructive topology neural networks, metaheuristic search algorithms) are studied in detail. The algorithm explanation is made enriched by discussing their technical merits, limitations, and applications in their respective categories. Finally, the authors recommend future new research directions which can contribute to strengthening the literature.

The FNN contributions are rapidly increasing because of its ability to make reliably informed decisions. Like learning algorithms, reviewed in Part I, the focus is to enrich the comprehensive study by reviewing remaining categories focusing on the optimization techniques. However, future efforts may be needed to incorporate other algorithms into identified six categories or suggest new category to continuously monitor the shift in the research trends.

The authors studied the shift in research trend for three decades by collectively analyzing the learning algorithms and optimization techniques with their applications. This may help researchers to identify future research gaps to improve the generalization performance and learning speed, and user to understand the applications areas of the FNN. For instance, research contribution in FNN in the last three decades has changed from complex gradient-based algorithms to gradient free algorithms, trial and error hidden units fixed topology approach to cascade topology, hyperparameters initial guess to analytically calculation and converging algorithms at a global minimum rather than the local minimum.

The existing literature surveys include comparative study of the algorithms, identifying algorithms application areas and focusing on specific techniques in that it may not be able to identify algorithms categories, a shift in research trends over time, application area frequently analyzed, common research gaps and collective future directions. Part I and II attempts to overcome the existing literature surveys limitations by classifying articles into six categories covering a wide range of algorithm proposed to improve the FNN generalization performance and convergence rate. The classification of algorithms into six categories helps to analyze the shift in research trend which makes the classification scheme significant and innovative.

The purpose of the paper is twofold. Firstly, this study aims to investigate the factors influencing stakeholders’ intention to invest in Shariah-compliant social impact bonds (SC SIBs) in Malaysia. Secondly, this study compares the differences in the perception of different stakeholders on the importance of the factors.

Using the extended theory of planned behaviour, the study undertakes a questionnaire survey on licensed capital market investors and individuals involved in the development of the financial market (developers). A total of 260 complete and valid responses were obtained from the survey. Multiple regression and Mann–Whitney tests were carried out to achieve the two objectives, respectively.

The results reveal that attitude (β = 0.447, p < 0.01), subjective norm (SN) (β = 0.255, p < 0.01) and moral norm (MN) (β = 0.163, p < 0.01) are significantly positive predictors of intention to invest in SC SIBs. In terms of the differences in the perceptions of the two parties, the results show that the factors have more effect towards developers than investors.

The empirical evidence from this study on the factors that influence stakeholders’ participation in SC SIBs is useful to the policymakers and interested parties in taking the next steps to develop, implement and promote SC SIBs to stakeholders in Malaysia. Fund managers can use the study’s insights to promote positive attitudes, SNs and MNs towards SC SIBs, especially targeting developers who are more influenced by these factors. More importantly, the results indicate a need for different strategies to influence the stakeholder investment behaviour of SC SIB in Malaysia to ensure that it is sustainable and viable in the long run.

The purpose of this paper is to present a test of scales that measure brand equity and its two dimensions – brand image and brand awareness – in the context of logistics services. The scales are tested with both logistics service providers and customers.

Measurement items are adapted from existing scales found in the marketing literature. Academic colleagues and logistics practitioners reviewed the items for face validity and readability. The scales are evaluated for reliability, convergent validity, and discriminant validity using data collected in a mail survey of logistics service providers and customers.

Findings suggest that brand awareness, brand image, and brand equity scales are valid and reliable in the context of logistics services.

While there is a substantial research stream that examines branding of consumer goods and an increasing literature on industrial and service brands, little is known about branding in the context of logistics services. This paper extends existing measurement of brand equity and its dimensions to a new setting, namely logistics services.

This paper provides valuable insight into the measurement of brand awareness, brand image, and brand equity in the logistics services context and offers a foundation for future logistics branding research. The paper provides evidence for the validity of constructs used in the customer‐based brand equity framework, which is traditionally used in consumer contexts, in the context of logistics services.