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The authors of this paper contend that too many firms' innovation initiatives are shackled with archaic budgeting and planning methodologies that are intended to protect managers from the embarrassment of blown budgets, missed deadlines, or market flops but instead suppress learning and adaptability, both critical to achieving successful commercialization of unique ideas. This paper aims to address this issue.

The authors propose that the first step to rid myopia and rigidity from the stage‐gate approach is to re‐conceive it as an assumption‐driven process centered on learning, rather than simply a sequence of activities marching towards a pre‐determined outcome.

The authors suggest that firms should adopt assumption‐driven learning in a series of sequential divergent‐convergent cycles – one cycle per stage – each centered on testing the major assumptions for that stage.

Continuous learning and unlearning is essential to the process of developing raw ideas into viable commercial applications. The key to success is to test assumptions through real‐life experiments – for example, market assumptions should be tested in‐market, manufacturability assumptions should be tested in production.

Firms should adopt assumption‐driven learning in a series of sequential divergent‐convergent cycles – one cycle per stage – each centered on testing the major assumptions for that stage.

This paper seeks to describe an approach to staff development and workforce planning which is implemented as part of a process of internal reviews of service areas within an academic library.

The paper describes the factors leading to the development of such a process within the Deakin University Library.

A number of different factors – internal to the library, at the university level and in the wider environment – have driven the need for such reviews and influenced their design. The approach developed focuses on comparing current workforce capabilities (competencies and resource levels) with the set of competencies and resources required to deliver the projected services to the standards required. This account highlights the links between the review process and the implementation of a library‐wide staff development framework.

A number of practical implications may be drawn: the value of a flexible approach taking into account the local and institutional environment; the critical importance of organizational needs driving individual staff development and the benefits of mapping links from strategic goals to staff development.

The paper focuses on an emerging process for service delivery in an academic library.

Building to order enables manufacturing to better respond to market conditions. The time lost between changes in customer preferences and product mix disappears and customer demand can both be anticipated and shaped by the sales system. An automotive build to order supply chain must be able to meet seasonality within markets, and understand the detailed demand volatility for certain elements of the complex product mix, from which much of the profitability is derived. Market responsive manufacturing entails adaptive and flexible production and supply capability in conjunction with real‐time market interaction through revenue management. The combination of late capacity setting and revenue management can enable the whole extended enterprise to operate as a single entity. This article outlines the demand volatility examined for automotive products, integrated revenue and demand management as a solution, and the subsequent order system investment requirements.

The purpose of this paper is to conduct a structured literature review using advanced bibliometric tools to understand the existing knowledge base, understand the trends in omni-channel (OC) research and identify emerging research topics.

More than 500 articles selected through a keyword combination search from reputed databases of peer-reviewed academic sources from period 2009–19 are analyzed for the purposes of this study. The study first presents an exploratory analysis to determine influential authors, sources and regions, among other key aspects. Second, several network analyses including co-citation and dynamic co-citation network analyses are conducted to identify themes. These allow identifying research clusters and emerging research topics algorithmically. Both centrality and modularity-based clustering are employed. A content analysis of the most influential groups within OC literature for each cluster is included.

The findings of this paper make unique contributions by using advanced tools from network analysis along with the standard bibliometric analysis tools to explore the current status of OC research, identify existing themes and the guidance for potential areas of future research interest in OC.

This research provides a comprehensive view of the range of topics of importance that have been discussed in the literature of OC management. These research trends can serve as a quick guide to researchers and practitioners to improve decision making and also develop strategies.

The paper employs advanced tools for the first time to review the literature of OC retailing. The sophisticated tools include co-citation and dynamic co-citation network analysis.

This chapter argues that one of the fundamental challenges of the global character of strategy research is the growing need to foster collaborations between academic and business practitioners that can help build a better understanding of the practice of strategy and through these means deliver greater impact. This challenge strengthens existing calls for strategy research to refocus on understanding the practice of strategy with an attentiveness to micro-dynamics of strategizing, and requires us to expand the ways in which research practice is performed. Whilst this can apparently be achieved through better dialogue, building trusting relationships and valuing the contribution each party can make due to their differences, it in fact requires a questioning of our research assumptions and practice.

The purpose of this paper is to report on the results of research into the precedence of the maturity factors, or key turning points in business process maturity (BPM) implementation efforts. A key turning point is a component of BPM that stabilizes within an organization and leads to the next maturity level.

Several years of data from over 1,000 companies in the USA, Europe, China, and Brazil that have completed a BPM assessment are analyzed to identify which components of BPM stabilize, when and in what order. Different analysis methods are employed in order to identify global commonalities and differences.

The paper identifies key turning points from several different perspectives using several different approaches and develops some conclusions common to all methods used in this research.

The relationship between the components (dependencies) is only suggested but not statistically analyzed. Several data sets are also on the low end of sample size for the methods used and some parts of the research used ad hoc selection of companies of arbitrarily distributed companies into different groups.

The results can be useful for leaders and teams that are attempting the journey to process maturity. The guide‐posts, milestones, and measures can help answer the question “Where am I on this journey and what is next?”

A plethora of maturity models has emerged that claim to guide an organization through the process of building levels of maturity that lead to competitive advantage. To date, there has been a lack of quantitative studies documenting these road‐maps. The paper provides global, quantitative evidence of the critical maturity components associated at each level of maturity.

The purpose of this paper is to develop a holistic model for educational purposes using data‐mining techniques for exploring the effects of probable changes in processes related to admissions, course delivery and recruitments.

The paper proposes a framework for an effective educational process using data‐mining techniques to uncover the hidden trends and patterns and making accuracy based predictions through a higher level of analytical sophistication in the process of counselling students.

Data‐mining tools are used in academia for capitalizing on the advances of information technology. This process improves research and academic decision making through uncovering hidden trends and patterns that predict using a combination of explicit knowledge base, sophisticated analytical skills and academic domain knowledge.

The paper presents a model using a data‐mining approach for academics.

The purpose of this chapter is to study the mathematisation of finance – excessive use of mathematical models in finance – which has been widely blamed for the recent financial and economic crisis. We argue that the problem might actually be the financialisation of mathematics, as evidenced by the gradual embedding of branches of mathematics into financial economics. The concept of embeddedness, originally proposed by Polanyi, is relevant to describe the sociological relationship between fields of knowledge. After exploring the relationship between mathematics, finance and economics since antiquity, we find that theoretical developments in the 1950s and 1970s lead directly to this embedding. The key implication of our findings is the realization that it has become necessary to disembed mathematics from finance and economics, and proposes a number of partial steps to facilitate this process. This chapter contributes to the debate on the mathematisation of finance by uniquely combining a historical approach, which chronicles the evolution of the relation between mathematics and finance, with a sociological approach from the perspective of Polyani’s concept of embedding.