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Customer engagement is of critical interest to both academics and practitioners. Extant literature focusses primarily on customer engagement with a single focal object, usually brands; this study takes another view to consider customer engagement with multiple focal objects (service provider and context). In addition to testing the relationship of the individual dimensions of engagement with the service provider and engagement with the context, this research elaborates on their drivers, with a particular focus on distinct engagement platforms. The paper aims to discuss these issues.

A survey captures customer engagement with a service provider and a context in a higher education setting, with 251 responses collected across first- and third-year marketing courses in an Australian, mid-sized university.

Engagement with the service provider can drive engagement with the context. In turn, engagement with the service provider can be stimulated through the use of engagement platforms that enable customer-to-service provider interactions. The results show limited effects of customer-to-customer engagement platforms on engagement with the context though. The results are consistent across gender and student grade levels; some differences arise between international and domestic students.

This unique study broadens understanding of customer engagement with various focal objects and also details the flow of effects, from engagement with a service provider to engagement with the context. This research builds on conceptual discussions of engagement platforms and empirically examines their ability to facilitate affective, cognitive and behavioural engagement.

Despite recognition that organizations operate in interrelated service systems, extant literature has focused strongly on dyadic engagement relationships (e.g. customer-to-brand). Taking into account the multiple engagement foci that exist within a service system, the purpose of this paper is to examine the interdependence among engagement with these multiple foci in a higher education setting. Specifically, the research investigates different configurations of engagement dimensions with the service provider and brand as they pertain to engagement with the study context.

A total of 251 students were surveyed in regards to their engagement with a service provider (lecturer), brand (university) and study context. Data analysis utilized Fuzzy set qualitative comparative analysis to identify the unique combinations of causal condition consistent with high student engagement with the study context.

Five solutions were identified, each with a different constellation of engagement dimensions. Most solutions entailed engagement with both the service provider and the brand, and cognitive processing (service provider) emerged as a core condition for every solution. This suggests service providers should seek to engage with consumers, particularly from a cognitive perspective, understanding this will support engagement with the context of study.

This research provides evidence that students can engage with their study context through different configurations of engagement with the service provider and the brand. Thus, it demonstrates the need to examine constellations of engagement dimensions related to multiple focal objects to understand their interdependencies and potential influence on engagement at a higher level of aggregation in a complex service environment.

While businesses seek to engage customers, their efforts are often met with varied results, as some customers are more predisposed to engage than others. Understanding customers’ dispositions to engage is central to understanding customer engagement, yet research examining customer engagement dispositions remains sparse and predominantly focused on personality traits. This paper aims to consider the general nature of a disposition and draws on qualitative findings to depict a framework for customer engagement dispositions.

To investigate customer engagement dispositions comprehensively and in-depth, an exploratory qualitative approach was adopted. In total, 20 semi-structured in-depth interviews were conducted with customers in ongoing relationships with financial planners residing in Australia.

Nine attributes reflecting customer engagement dispositions emerge from the data. These include the customer’s internal tendency to engage (confidence, desire for control, extroversion and enthusiasm); a tendency to engage determined in the interaction with the service provider (sense of similarity, sense of social connection and trust in the service provider); and the capacity to engage (expertise and knowledge and time availability).

This study provides a conceptual foundation for future empirical measurement of customer engagement dispositions and their nomological network.

This study establishes a foundation for managers to build distinct engagement disposition profiles and segments and target initiatives to maximize engagement activity.

This research challenges the view of customer engagement dispositions as largely personality factors, or exclusively cognitive and emotional dimensions of engagement, and offers a comprehensive framework reflecting a customer’s disposition to engage with a service provider.

The purpose of this paper is to investigate the applicability of a direct current (DC) hysteresis measurement on power transformer terminals for the subsequent hysteresis model parametrization in transformer grey box topology models.

Two transformer topology models with two different hysteresis models are used together with a DC hysteresis measurement via the power transformer terminals to parameterize the hysteresis models by means of an optimization. The calculated current waveform with the derived model in the transformer no-load condition is compared to the measured no-load current waveforms to validate the model.

The proposed DC hysteresis measurement via the power transformer terminals is suitable to parametrize two hysteresis models implemented in transformer topology models to calculate the no-load current waveforms.

Different approaches for the measurement and utilization of transformer terminal measurements for the hysteresis model parametrization are discussed in literature. The transformer topology models, derived with the presented approach, are able to reproduce the transformer no-load current waveform with acceptable accuracy.

This study aims to propose a decision support system (DSS) that performs a decision-based part-fixture assignment and fixture flow control in planned production periods.

The principal approaches were fuzzy case-based reasoning (FCBR) and discrete-event simulation (DES). Besides, the fuzzy analytic hierarchy process (FAHP), an object-oriented (OO) method and a fuzzy weighted Euclidean distance were used to support the decision-making process.

It shows that integrating FCBR and DES systems is a promising approach to address part-fixture planning problems. The FCBR subsystem proposed various stable numbers of fixtures as scenarios. The DES model analyzed the future performances of these scenarios and identified the best alternative.

The DSS was tested in laboratory environments using a numerical analysis; however, it was not validated in industrial situations.

The synergy of integrating FCBR and DES systems was not exploited in the past in part-fixture assignment and fixture flow control problems.

The purpose of this paper is to propose a decision support system (DSS) that stabilizes the flow of fixtures in manufacturing systems. The proposed DSS assists decision-makers to reuse or adapt the available fixtures or to manufacture new fixtures depending upon the similarity between the past and new cases. It considers the cost effectiveness of the proposed decision when an adaptation decision is passed.

The research problem is addressed by integrating case-based reasoning, rule-based reasoning and fuzzy set theory. Cases are represented using an object-oriented (OO) approach to characterize them by their feature vectors. The fuzzy analytic hierarchy process (FAHP) and the inverse of weighted Euclidean distance measure are applied for case retrieval. A machining operation is illustrated as a computational example to demonstrate the applicability of the proposed DSS.

The problems of fixture assignment and control have not been well-addressed in the past, although fixture management is one of the complex problems in manufacturing. The proposed DSS is a promising approach to address such kinds of problems using the three components of an artificial intelligence and FAHP.

Although the DSS is tested in a laboratory environment using a numerical example, it has not been validated in real industrial systems.

The DSS is proposed in terms of simple rules and equations. This implies that it is not complex for software development and implementation. The illustrated numerical example indicates that the proposed DSS can be implemented in the real-world.

Demand-driven fixture retrieval and manufacture to assign the right fixtures to planned part-orders using an intelligent DSS is the main contribution. It provides special consideration for the adaptation of the available fixtures in a system.

Cloud manufacturing platform has a high degree of openness, with a large variety of users having different needs. Designers on such platforms exhibit great differences in their knowledge abilities and knowledge needs, necessitating the cloud platform to provide personalized knowledge recommendation. To satisfy the personalized knowledge needs of the designers in product design tasks and other manufacturing tasks on a cloud manufacturing platform and provide them with high-quality knowledge resources, a knowledge recommendation method based on designers’ knowledge ability is proposed. The proposed method, with appropriate adjustments, can also be used for personalized knowledge recommendation to other personnel or institutions in cloud manufacturing platforms.

A knowledge recommendation method model is developed. The method consists of three stages. First, a designer knowledge system is constructed based on customer reviews in historical tasks, and designer knowledge ability and knowledge demand degree are quantitatively evaluated by synthesizing customer reviews and expert evaluations. Subsequently, the design knowledge domain ontology is constructed, and knowledge resources and tasks are modeled based on the ontology. Finally, the semantic similarity between tasks and knowledge resources and the knowledge demand degree of designers are integrated to calculate the knowledge recommendation coefficient, which realizes the personalized knowledge recommendation of designers.

Two design tasks of a 3D printing cloud platform are taken as examples to verify the feasibility and effectiveness of the proposed method. Compared with other methods, it is proved that the method proposed in this paper can obtain more knowledge resources that meet the needs of designers and tasks.

The method proposed in this paper is important for the expansion of data applications of the cloud manufacturing platform and for enriching the knowledge recommendation method. The proposed method has two innovations. First, both designer needs and task needs are considered in knowledge recommendation. Compared with most of the existing methods, which only consider one factor, this method is more comprehensive. Second, the designer’s knowledge ability model is constructed by using customer reviews on the cloud manufacturing platform. This overcomes the defect of low accuracy of the interest model in existing methods and makes full use of the big data of the cloud manufacturing platform.

This paper aims to propose an energetic model parameter calculation method for predicting the materials’ symmetrical static hysteresis loop and asymmetrical minor loop to improve the accuracy of electromagnetic analysis of equipment.

For predicting the symmetrical static hysteresis loop, this paper deduces the functional relationship between magnetic flux density and energetic model parameters based on the materials’ magnetization mechanism. It realizes the efficient and accurate symmetrical static hysteresis loop prediction under different magnetizations. For predicting the asymmetrical minor loop, a new algorithm is proposed that updates the energetic model parameters of the asymmetrical minor loop to consider the return-point memory effect.

The comparison of simulation and experimental results verifies that the proposed parameters calculation method has high accuracy and strong universality.

The proposed parameter calculation method improves the existing parameter calculation method’s problem of relying on too much experimental data and inaccuracy. Consequently, the presented work facilitates the application of the finite element electromagnetic field analysis method coupling the hysteresis model.

Today’s in-demand skills may not be needed tomorrow. As companies are adopting a new group of technologies, they are in huge need of information technology (IT) professionals who can fill various IT positions with a mixture of technical and problem-solving skills. This study aims to adopt a sematic analysis approach to explore how the US Information Systems (IS) programs meet the challenges of emerging IT topics.

This study considers the application of a hybrid semantic analysis approach to the analysis of IS higher education programs in the USA. It proposes a semantic analysis framework and a semantic analysis algorithm to analyze and evaluate the context of the IS programs. To be more specific, the study uses digital transformation as a case study to examine the readiness of the IS programs in the USA to meet the challenges of digital transformation. First, this study developed a knowledge pool of 15 principles and 98 keywords from an extensive literature review on digital transformation. Second, this study collects 4,093 IS courses from 315 IS programs in the USA and 493,216 scientific publication records from the Web of Science Core Collection.

Using the knowledge pool and two collected data sets, the semantic analysis algorithm was implemented to compute a semantic similarity score (DxScore) between an IS course’s context and digital transformation. To present the credibility of the research results of this paper, the state ranking using the similarity scores and the state employment ranking were compared. The research results can be used by IS educators in the future in the process of updating the IS curricula. Regarding IT professionals in the industry, the results can provide insights into the training of their current/future employees.

This study explores the status of the IS programs in the USA by proposing a semantic analysis framework, using digital transformation as a case study to illustrate the application of the proposed semantic analysis framework, and developing a knowledge pool, a corpus and a course information collection.

Using China’s key audit matters (KAMs) data, this study aims to examine whether negative press coverage alleviates boilerplate KAMs.

This study uses Levenshtein edit distance (LVD) to calculate the horizontal boilerplate of KAMs and investigates how boilerplate changes under different levels of the perceived legal risk.

The findings indicate that auditors of firms exposed to substantial negative press coverage will reduce the boilerplate of KAMs. This association is more significant for auditing firms with lower market share and client firms with higher financial distress. Additionally, the authors find that negative press coverage is more likely to alleviate the boilerplate disclosure of KAMs related to managers’ subjective estimation and material transactions and events. Furthermore, the association between negative press coverage and boilerplate KAMs varies with the source of negative news.

The findings suggest that upon exposure to negative press coverage, reducing the boilerplate of KAMs has a disclaimer effect for auditors.