Search results

The aim of this paper is to identify key issues and successful patterns of collaborative customer relationship management (CRM) in financial services networks.

The study takes the form of a multi‐case analysis.

The paper finds that key issues of CRM in financial services networks are redundant competencies of partnering companies, privacy constraints, CRM process integration, customer information exchange, and CRM systems integration. To address these issues, partnering companies have to agree on clear responsibilities in collaborative processes. Data privacy protection laws require that customer data transfer between partnering companies has the explicit approval of customers. For process integration, companies have to agree on process standards and a joint integration architecture. Web services and internet‐based standards can be used for inter‐organizational systems integration. Data integration requires the development of a joint data model. Either a unique customer identification number or a matching algorithm must be used to consolidate customer data records of partnering companies.

Because of the limited number of case studies, generalizability is limited. The findings can serve as a starting point for researchers seeking to further explore the topic with quantitative methods.

The findings can be used by financial services networks to improve their collaborative CRM approaches.

The importance of collaborative CRM in business networks is likely to increase due to the continuing deconstruction of value chains not only in the financial services industry, but in other industries as well. Nevertheless, the topic has not received much attention in research.

The role of big data and data analytics in the audit engagement process is evident. Notwithstanding, understanding how big data influences cognitive processes and, consequently, on the auditors’ judgment decision-making process is limited. The purpose of this paper is to present a conceptual framework on the cognitive process that may influence auditors’ judgment decision-making in the big data environment. The proposed framework predicts the relationships among data visualization integration, data processing modes, task complexity and auditors’ judgment decision-making.

The methodology to accomplish the conceptual framework is based on a thorough literature review that consists of theoretical discussions and comparative studies of other authors’ works and thinking. It also involves summarizing and interpreting previous contributions subjectively and narratively and extending the work in some fashion. Based on this approach, this paper formulates four propositions about data visualization integration, data processing modes, task complexity and auditors’ judgment decision-making. The proposed framework was built from cognitive theory addressing how auditors process data into useful information to make judgment decision-making.

The proposed framework expects that the cognitive process of data visualization integration and intuitive data processing mode will improve auditors’ judgment decision-making. This paper also contends that task complexity may influence the cognitive process of data visualization integration and processing modes because of the voluminous nature of data and the complexity of business processes. Hence, it is also expected that the relationships between data visualization integration and audit judgment decision-making and between processing mode and audit judgment decision-making will be moderated by task complexity.

There is a dearth of studies examining how big data and big data analytics affect auditors’ cognitive processes in making decisions. This paper will help researchers and auditors understand the behavioral consequences of data visualization integration and data processing mode in making judgment decision-making, given a certain level of task complexity.

With the advent of big data and the evolution of innovative audit procedures, the constructed framework can be used as a theoretical foundation for future empirical studies concerning auditors’ judgment decision-making. It highlights the potential of big data to transform the nature and practice of accounting and auditing.

Based on organizational information processing theory, this research explores how big data analytics capability (BDAC) contributes to green supply chain integration (GSCI) and the contingency role that data-driven decision culture plays.

Using the two-wave survey data collected from 317 Chinese manufacturing firms, the authors validate the hypotheses.

The results show that big data managerial capability has positive impacts on three dimensions of GSCI, while big data technical capability has positive impacts on green internal and customer integration. Moreover, green internal integration mediates the impacts of big data technical capability and managerial capability on green supplier and customer integration. Finally, data-driven decision culture alleviates the positive impacts of big data technical and managerial capability on green internal integration.

The findings suggest that firms can leverage big data technical and managerial capability to enhance information processing capability for achieving a higher degree of GSCI. Further, the critical role of data-driven decision culture in affecting the link between BDAC and GSCI should not be overlooked.

This research contributes to literature on green supply chain management by revealing the role of BDAC in improving GSCI.

This study demonstrates integration within a mixed-methods case study of construction phenomena, whilst ensuring reliability and validity. This is in view of the established philosophical challenges in theory generation, whereby qualitative and quantitative methods are underpinned by divergent, almost incompatible, paradigmic assumptions.

The study uses a sample case study research on the phenomenon of cost overruns, supported by a coherent flow of well-articulated philosophical arguments to idealise the logic of integration. Issues of reliability and validity were resolved along these lines, by incorporating applicable criteria from both the qualitative and qualitative strands. A detailed outline and rationalisation of the stepwise approach to achieving integration are provided, from the point of design conceptualisation, data collection, analysis and further down to theory generation.

The study generated two level-1 theories by collecting numerical data on cost overruns, geotechnical index parameters and textual data on the geotechnical practices. Another level-1 theory was generated in reflexive adaptation to unanticipated social constructs emerging from the qualitative data. All level-1 theories from the quantitative and qualitative strands were triangulated to yield two “level-2 theories”: A log-regression model and a cognitive map. The approach to integration is thus explanatory sequential, and concurrent (at the second stage of transformation in the generation of level-2 theories).

The study empirically reinforces that ontological flexibility, achievable through the use of thoughtfully designed integrated mixed-methods case studies, permits the investigation of multidimensional construction phenomena in innovative ways, relevant to provide holistic theoretical and practice-based contributions.

The study practically signposts a bespoke stepwise approach to integration, in a mixed-methods case study of construction phenomena, against the contextual backdrop of its relative novelty and lack of studies delving in-depth into the theoretical nitty-gritty.

The purpose of this paper is to investigate whether business process integration is feasible.

This paper employs a single case study strategy to research the aforementioned research question. The case study is exploratory.

Based on the findings and within the context of the case organisation, it appears that enterprise application integration (EAI) technology can integrate business processes. However, since it is not possible to generalize from a single case study, further research is suggested to investigate this area. From the case study, it appears that EAI can easily integrate the business processes when it is combined with enterprise resource planning (ERP) systems.

This is a single case study and thus the results cannot be generalized.

The empirical date suggest that organisations may combine ERP with EAI to integrate their business processes in a more flexible way.

The contribution of the paper is threefold: it describes the business process automation layer of EAI technology, it defines and presents a stage model for the business process integration and it examines the research question.

Data integration is to combine data residing at different sources and to provide the users with a unified interface of these data. An important issue on data integration is the existence of conflicts among the different data sources. Data sources may conflict with each other at data level, which is defined as data inconsistency. The purpose of this paper is to aim at this problem and propose a solution for data inconsistency in data integration.

A relational data model extended with data source quality criteria is first defined. Then based on the proposed data model, a data inconsistency solution strategy is provided. To accomplish the strategy, fuzzy multi-attribute decision-making (MADM) approach based on data source quality criteria is applied to obtain the results. Finally, users feedbacks strategies are proposed to optimize the result of fuzzy MADM approach as the final data inconsistent solution.

To evaluate the proposed method, the data obtained from the sensors are extracted. Some experiments are designed and performed to explain the effectiveness of the proposed strategy. The results substantiate that the solution has a better performance than the other methods on correctness, time cost and stability indicators.

Since the inconsistent data collected from the sensors are pervasive, the proposed method can solve this problem and correct the wrong choice to some extent.

In this paper, for the first time the authors study the effect of users feedbacks on integration results aiming at the inconsistent data.

Matching relevant ontology data for integration is vitally important as the amount of ontology data increases along with the evolving Semantic web, in which data are published from different individuals or organizations in a decentralized environment. For any domain that has developed a suitable ontology, its ontology annotated data (or simply ontology data) from different sources often overlaps and needs to be integrated. The purpose of this paper is to develop intelligent web ontology data matching method and framework for data integration.

This paper develops an intelligent matching method to solve the issue of ontology data matching. Based on the matching method, it also proposes a flexible peer‐to‐peer framework to address the issue of ontology data integration in a distributed Semantic web environment.

The proposed matching method is different from existing data matching or merging methods applied to data warehouse in that it employs a machine learning approach and more similarity measurements by exploring ontology features.

The proposed method and framework will be further tested for some more complicated real cases in the future.

The experiments show that this proposed intelligent matching method increases ontology data matching accuracy.

Despite many years of academic research into organisational integration and effectiveness, organisations still struggle to successfully implement strategy and achieve competitive advantage. However, the rapid evolution of marketing technologies such as big data, marketing analytics, artificial intelligence and personalised consumer interactions offer potential for an integrated marketing communication technological capability that aligns and integrates an organisation. Programmatic advertising is one such integrated marketing communication (IMC) technology capability, applying and learning from customer information and behaviours to align and integrate organisational activity. The literature on programmatic is embryonic and a conceptual framework that links its potential to organisational effectiveness is timely. This paper aims to develop a framework showing the potential for programmatic advertising as an IMC technology capability to enhance organisational integration and performance.

An exploratory methodology gained insight from 15 depth interviews with senior marketing executives from both organisations and external advertising agencies.

Four elements of a programmatic integrated organisation were identified and aligned with seven marketing activity levers to deliver firm performance measures.

This research contributes to theory, affirming IMC as a capability and positioning programmatic as a means of organisational integration.

The model also offers guidance for practitioners looking to integrate programmatic into their organisation.

To the best of the authors’ knowledge, this is the first paper to look at programmatic from an IMC perspective and as a means of organisational integration. It is also the first to apply Moorman and Day’s (2016) model to explore organisational integration and programmatic, developing a new model, specifically contextualised for programmatic advertising.

The purpose of this study is to examine the position of interoperability of government and corporate portals in technological adoption space in India in terms of three critical dimensions: data integration, process integration and communication integration.

This exploratory study was conducted through a survey questionnaire from 300 portals of government departments and public sector undertakings (PSUs) in India. Data were also collected from portals of Indian companies and the results have been compared with those of the government portals.

The results show that the majority of government portals in India have initiated integration. Second, the portals of Indian companies are performing better than the portals of government and PSUs for achieving an interoperable position. Third, there is high dispersion in level of integration of government portals in India.

The portals with the lowest level of integration in government in India will determine when government will actually attain full horizontal integration and hence achieve an interoperable portal as there is high dispersion in level of integration of government portals in India. Also, for achieving an interoperable government portal, an organization needs to focus on the weakest factors of each dimension.

This study is the first to examine the position of interoperability in technological adoption space in India. The results lead to a number of recommendations for achieving interoperability for government portals in India. The study also highlights the weakest factors of each dimension that require more improvement than other factors.

Many business process improvement efforts emphasize better integration, yet process integration can mean many things. The purpose of this paper is to emphasize the importance of information flows to modern business processes, and draw upon recent organizational and information systems literature to characterize process integration and to derive four principles of process integration: accessibility, timeliness, transparency, and granularity of information flows.

Using a field study, the four principles of process integration are applied to analyze ten different business processes across five organizations.

In total, 18 generalized activities are identified that describe non‐integrated behavior, and “keying in known data” was found to be the most common. Among other findings, analysis highlights the importance of documentation to modern business processes, especially for coordination roles, and the paper describes three different purposes for documentation found in the data: content, process validation, and posterity.

The articulation of “business process integration” offers a foundation for future research in this area. Findings are limited in generalizability to various levels of processes, as well as possible instrument‐related biases.

The principles of process integration provide a lens through which practitioners can analyze processes. Empirical findings stress the role of documentation, forms of documentation, and types of non‐integrated work.

The paper characterizes process integration in relation to other commonly‐used constructs such as organizational integration, data integration, and application integration. Principles are derived from the literature that can guide future inquiry and practice associated with business process improvement.