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Accurate prediction of order demand across omni-channel supply chains improves the management's decision-making ability at strategic, tactical and operational levels. The paper aims to develop a predictive methodology for forecasting near-real-time e-commerce order arrivals in distribution centres, allowing third-party logistics service providers to manage the hour-to-hour fast-changing arrival rates of e-commerce orders better.

The paper proposes a novel machine learning predictive methodology through the integration of the time series data characteristics into the development of an adaptive neuro-fuzzy inference system. A four-stage implementation framework is developed for enabling practitioners to apply the proposed model.

A structured model evaluation framework is constructed for cross-validation of model performance. With the aid of an illustrative case study, forecasting evaluation reveals a high level of accuracy of the proposed machine learning approach in forecasting the arrivals of real e-commerce orders in three different retailers at three-hour intervals.

Results from the case study suggest that real-time prediction of individual retailer's e-order arrival is crucial in order to maximize the value of e-order arrival prediction for daily operational decision-making.

Earlier researchers examined supply chain demand, forecasting problem in a broader scope, particularly in dealing with the bullwhip effect. Prediction of real-time, hourly based order arrivals has been lacking. The paper fills this research gap by presenting a novel data-driven predictive methodology.

In this chapter, I explore traditional notions of secondary data in qualitative research and consider the ways in which these are continually being reimagined in the digital age. I situate this discussion in respect to data typologies and, more reflexively, in relation to our need as researchers to make data real. I consider contemporary understandings of reuse in relation to secondary data, focusing particularly on qualitative interview data. Recognizing those who are already forging a path, I then suggest how we might move beyond notions of reuse and reimagine secondary data in the digital age. To illustrate these points, I highlight relevant studies drawing data from a range of online spaces, and finally summarize key considerations and challenges.

Integrating building information modelling (BIM) into facility management (FM) has generated fervent debate within the extant literature given an increasing realisation amongst practitioners that the majority of BIM benefits reside within the whole lifecycle management. Converting a building’s in-use data and information into tangible business knowledge to augment FM performance is crucial for business growth and prosperity. Yet curiously, scant academic attention has been paid in investigating the data and information hand-over process through BIM required by FM. The purpose of this paper is to address this knowledge gap.

The research approach adopted draws upon pertinent BIM in the FM literature and a qualitative discourse accrued via focus group meetings of Australian FM experts.

The study’s findings are presented as a data and information typology matrix that captures the associations among different forms of data ownership, FM service delivery categories and data and information typologies for using BIM in FM.

The analysis results will prove invaluable for policy-makers, researchers and practitioners by providing a relevant data and information typology and capturing the complex associations in the process of integrating BIM into FM.

This study contributes to the field by providing a better understanding of different forms of data and information, required by members of a FM team. The original insight shared here is the necessity to consider a distinctive array of interests and the potential for using different forms of data and information when integrating BIM into FM.

This study aims to propose the four different typologies for understanding local information.

This study applied a conceptual approach to analyze and clarify how the concept local information can be understood in wildly different ways. Furthermore, this study employed conceptual analysis of 36 studies. For the conceptual analysis, coding was applied to formulate and abstract four typologies for understanding local information with specific focus on the Thai cultural heritage setting.

The four different typologies include local information as an array of different interpretations as diverse meanings of local, local information as cultural heritage, local information as subject of information management and situated local information.

This study mainly focuses relevant typologies for understanding local information in the Thai context.

This study contributes and extends the literature in the local information field and the cultural heritage context. In addition, an eclectic strategy of using several alternative typologies for dealing with essentially contested concepts is suggested. This can be useful not only for supporting librarians working with local information but also in other practices dealing with broadly defined concepts.

Collaborative consumption firms leverage networked peers, communicating, collaborating and even delivering services to one another through a central marketplace channel. This raises questions as to the nature of this new form of digital channel strategy and deployment from a firm’s perspective. As a first step, this research seeks to help bridge the gap in knowledge by establishing an understanding of the digital channel usage of collaborative consumption firms.

A qualitative content analysis of 30 collaborative consumption firms was conducted using multiple data sources and coded into typologies against a predetermined coding scheme. These results were then compared against existing literature on digital channel usage in regards to a wider company usage.

This study identifies the digital channel usage and digital channel typology of each of the 30 firms associated within the collaborative consumption domain. The study shows a distinct increase in the use of social and community digital channels between traditional firms and collaborative consumption firms. As a result of this study, a concise definition of a collaborative consumption firm is provided, the digital channel usage of collaborative consumption firms is detailed and insights are provided for each sub-type of collaborative consumption.

This study contributes to the understanding of the collaborative consumption phenomena, the business model of collaborative consumption firms and digital channels. This study assists in describing the shift from traditional firms to peer-to-peer systems. Finally, a theoretical model is provided that demonstrates the nuance of collaborative consumption channel choice within each subcategory for future researchers to test and reflect upon.

This study demonstrates how collaborative consumption firms are allowing customers to drive interaction rather than traditional business-to-customer messages. A theoretical model is provided which shows contemporary marketers how to best dictate a digital channel strategy for a collaborative consumption style initiative.

Contributions include: a definition of what a collaborative consumption firm and its channels pertain to and how to design a collaborative consumption digital channel strategy. This study presents a digital channel comparison between collaborative consumption firms and traditional organisations.

The purpose of this paper is to provide a theoretical framework of how knowledge management (KM) systems can facilitate the incorporation of big data into strategic decisions. Advanced analytics are becoming increasingly critical in making strategic decisions in any organization from the private to public sectors and from for-profit companies to not-for-profit organizations. Despite the growing importance of capturing, sharing and implementing people’s knowledge in organizations, it is still unclear how big data and the need for advanced analytics can inform and, if necessary, reform the design and implementation of KM systems.

To address this gap, a combined approach has been applied. The KM and data analysis systems implemented by companies were analyzed, and the analysis was complemented by a review of the extant literature.

Four types of data-based decisions and a set of ground rules are identified toward enabling KM systems to handle big data and advanced analytics.

The paper proposes a practical framework that takes into account the diverse combinations of data-based decisions. Suggestions are provided about how KM systems can be reformed to facilitate the incorporation of big data and advanced analytics into organizations’ strategic decision-making.

This is the first typology of data-based decision-making considering advanced analytics.

The purpose of this paper is to contribute to the literature on intellectual capital (IC) in light of the emerging paradigm of Big Data. Through a literature review, this paper provides momentum for researchers and scholars to explore the emerging trends and implications of the Big Data movement in the field of IC.

A literature review highlights novel and emerging issues in IC and Big Data research, focussing on: IC for organisational value, the staged evolution of IC research, and Big Data research from the technological to the managerial paradigm. It is expected that identifying these contributions will help establish future research directions.

A conceptual multi-level framework demonstrates how Big Data validates the need to shift the focus of IC research from organisations to ecosystems. The framework is organised into four sections: “why” – the managerial reasons for incorporating Big Data into IC; “what” – the Big Data typologies that enhance IC practice; “who” – the stakeholders involved in and impacted by Big Data IC value creation; and “how” – the Big Data processes suitable for IC management.

The paper provides many avenues for future research in this emerging area of investigation. The key research questions posed aim to advance the contribution of Big Data to research on IC approaches.

The paper outlines the socio-economic value of Big Data generated by and about organisational ecosystems. It identifies opportunities for existing companies to renew their value propositions through Big Data, and discusses new tools for managing Big Data to support disclosing IC value drivers and creating new intangible assets.

This paper investigates the effects and implications Big Data offers for IC management, in support of the fourth stage of IC research. Additionally, it provides an original interpretation of IC research through the lens of Big Data.

A key issue in the literature about research libraries concerns their potential role in managing research data. The aim of this paper is to study the arguments for and against associating this task with libraries and the impact such an association would have on information professionals, and consider the competitors to libraries in this field.

This paper considers the nature of data and discusses data typologies, the kinds of data contained within databases and the implications of criticisms of the data-information-knowledge (DIK) hierarchy. It outlines the many competing agencies in the data curation field and describes their relationships to different kinds of data.

Many data are organically connected to the activities of large, domain-specific organizations; as such, it might be difficult for research libraries to assume a leadership role in curating data. It seems more likely that the qualifications of information professionals will come to be needed in such organizations and that the functions of research libraries will shift toward giving greater prevalence to their role as specialists in scholarly communication. In some cases, however, research libraries may be the best place to select, keep, organize and use research data. To prepare for this task, research libraries should be actively involved in domain-specific analytic studies of their respective domains.

This paper offers a theoretical analysis and clarification of the problems of data curating from the perspective of research libraries.

This study sheds light on a hitherto understudied group in family business literature: widows. We explore the roles a widow may take following the unexpected death of her owner-manager spouse when she had no salient role in the business prior to the death.

We used a qualitative approach to research, to study inductively the roles considered and taken by three widows who unexpectedly succeeded as owners of Swedish privately held family firms. We conducted semi-structured interviews with widows and children in top management.

We construct a typology of four main roles a widow can take and analyse the underlying dimensions that they represent. We also analyse to which extent the choice of role widow can be explained by psychological ownership and double-loss theory. The typology can be used as a tool for family business owners and their advisors as the basis of an open and non-prejudiced discussion of the choices available to a widow.

We have investigated the factors that influence a widow's decision whether to take over the business or not, as suggested in previous research by, for example, Martinez et al. (2009). We explore the roles a widow can consider and adopt. The study advances our understanding of how businesses can remain as family firms also in the event of the unexpected death of an owner-manager (De Massis et al., 2008). We hereby contribute to the literature on sudden successions and on women in family businesses.

The purpose of this study is to investigate factors that impede the integration of facilities management (FM) into building information modeling (BIM) technology. The use of BIM technology in the commercial construction industry has grown enormously in recent years. Its application to FM, however, is still limited. The literature highlights issues that hinder BIM–FM integration, which are studied and discussed in detail in this paper.

A review of literature is conducted to identify and categorize key issues hampering the application of BIM to FM. This paper has also designed a questionnaire based on a literature review and surveyed FM professionals at two industry events. Using the collected responses, these issues are analyzed and discussed using non-parametric statistical analyses.

A total of 16 issues are identified through the literature review of 54 studies under the four categories of BIM-execution and information-management, technological, cost-based and legal and contractual issues. The results of the survey of FM professionals (with 57 complete responses) reveal that the single most important issue is the lack of FM involvement in project phases when BIM is evolving.

The findings of this study could assist the construction industry (e.g. building-material and equipment manufacturers, design professionals, general contractors, construction managers, owners and facility managers) with creating guidelines that would help in BIM–FM integration. BIM is a virtual database that contains important design and construction information, which can be used for effective and efficient life cycle management if building data are captured completely and accurately with a facility manager’s involvement.