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The purpose of this study is to investigate sequences of event and the resulting innovation paths and trajectories followed by a university spin-off organization.

A single longitudinal case study methodology was applied to analyze innovation events and paths along the trajectory. Narrative methods were used to analyze actor perceptions on innovation processes/events.

The study categorizes events and paths in two categories, technical and commercialization, and finds that lock-in events matter for convergence of an innovation trajectory. The results indicate that understanding critical events may assist timely interventions in the innovation paths, thus potentially avoiding disruptions of the development of an innovation trajectory. The temporal processes reveal contrasting convergence–divergence patterns in the trajectory, depending on the types of events that occur.

Using a single case data may limit the applicability of the findings, which calls for future research.

Industries could monitor the technical and commercialization paths as a strategy to reduce “vulnerability” of the innovation trajectory and possible negative impacts. Knowledge about the role of the CEO is key for a university spin-off organization.

This study presents a new typology of events and paths, identifies and characterizes lock-in events and shows the relatively fragile dexterity between convergent and divergent paths along an innovation trajectory.

This paper systematically evaluates the existing literature of innovations in freight transport, including all modes, to uncover the key research themes and methodologies employed by researchers to study innovations and their implications in this industry. It analyses the role of transport and the impact of innovations during crises, such as COVID-19.

Qualitative and quantitative analysis of the innovations in freight transport unravels the pre-requisites of such endeavours in achieving a resilient and sustainable transport network that effectively and efficiently operates during a crisis. The authors performed keyword co-occurrence network (KCON) analysis and research focus parallelship network (RFPN) analysis using BibExcel and Gephi to determine the major resulting research streams in freight transport.

The RFPN identified five emerging themes: transport operations, technological innovation, transport economics, transport policy and resilience and disaster management. Optimisation and simulation techniques, and more recently, artificial intelligence and machine learning (ML) approaches, have been used to model and solve freight transport problems. Automation innovations have also penetrated freight and supply chains. Information and communication technology (ICT)-based innovations have also been found to be effective in building resilient supply chains.

Given the growth of e-commerce during COVID-19 and the resulting logistics demand, along with the need for transporting food and medical emergency products, the role of automation, optimisation, monitoring systems and risk management in the transport industry has become more salient. Transport companies need to improve their operational efficiency using innovative technologies and data science for informed decision-making.

This paper advises researchers and practitioners involved in freight transport and innovation about main directions and gaps in the field through an integrated approach for evaluating research undertaken in the area. This paper also highlights the role of crisis, e.g. COVID-19, and its impacts on freight transport. Major contributions of this paper are as follows: (1) a qualitative and quantitative, systematic and effective assessment of the literature on freight transport through a network analysis of keywords supplemented by a review of the text of 148 papers; (2) unravelling major research areas; (3) identifying innovations in freight transport and their classification as technological and non-technological and (4) investigating the impact of crises and disruptions in freight transport.

The recent surge in behavioral studies on the coordination mechanisms in supply chains (SCs) and advanced methods highlights the role of SC coordination (SCC) and behavioral issues associated with improving the performance of the operations. This study aims to critically review the behavioral aspect of channel coordination mechanisms.

Following a systematic literature review methodology, the authors adopt a combination of bibliometric (to reflect the current state of the field), content (using Leximancer data mining software to develop thematic maps) and theory-oriented qualitative analyzes that provide a holistic conceptual framework to unify the literature’s critical concepts.

The analysis confirms the plethora of risk-oriented publications, demonstrating that the second largest category of studies is concerned with social preferences theory. Most studies were based on experiments, followed by analytical modeling, revealing the impact of heuristics and individual preferences in SC decisions and suggesting promising managerial and theoretical avenues for future research.

The study sheds light on behavioral decision theories applied to SC coordination by categorizing the literature based on the adopted theories. The methodological contributions include using automated content analysis and validating the outcome by interviewing leading scholars conducting active research on “behavioral operations management and SC contracts.” The authors also propose several directions for future research based on the research gaps.

This chapter outlines a method for developing simulation code from case-based data using narrative sequence analysis. This analytical method allows researchers to systematically specify the ‘real-world’ behaviours and causal mechanisms that describe the research problem and translate this mechanism into simulation code. An illustrative example of the process used for code development from case-based data is detailed using a well-documented case of photovoltaic innovation. Narrative sequence analysis is used to analyse case data. Micro-sequences are identified and simplified. Each micro-sequence is presented first in pseudo-code and then in simulation code. This chapter demonstrates the coding process using Netlogo code. Narrative sequence analysis provides a rigorous and systematic approach to identifying the underlying mechanisms to be described when building simulation models. This analytical technique also provides necessary and sufficient information to write simulation code. This chapter addresses a current gap in the methodology literature by including case data within agent-based model building processes. It benefits B2B marketing researchers by outlining guiding processes and principles in the use of case-based data to build simulation models.

The paper aims to fill a gap in the literature in relation to the determinants of customer value within the research and development (R&D) industry and word‐of‐mouth. It investigates whether context specific variables, such as organizational type and contract length, change customer value evaluations and the value – intention to repurchase – recommend system.

A survey of Australian customers of a research and development service organization was conducted. Structural equation modelling was used to develop a model investigating factors that affect customer value, intent to re‐purchase, and word‐of‐mouth/recommendation.

Relationship benefits, service benefits and sacrifice all had a significant influence on customer value. Efficient use of time is crucial for sacrifice evaluation. Relationship benefits were larger for government organizations than private organizations. Importance of value to recommend the organization to others was higher for longer contracts and government customers.

Results show that R&D organizations need to concentrate on both the technical service/science aspects and the relationship aspects in their contracts. Government institutions tended to emphasize the relationship benefits while private businesses considered service benefits, relationship benefits and sacrifice nearly equal in their determination of value. Intent to contract R&D organization for further work and the willingness to recommend it to others as a highly specialized and competent service provider seem to be higher for government customers and longer contracts.

This paper investigates customer value in a little researched industry, R&D. The findings are relevant for similar professional business‐to‐business services.

This article aims to describe patterns of change in innovation networks and to clarify the roles of time and history in shaping network trajectories. The authors test seven predictor variables and their interactions to examine their influences on network performance over time.

A fuzzy simulation of innovation networks and investigations of different network types, using two classes of growth modeling techniques, help refine understanding of innovation as an interactive, developmental process.

Innovation network trajectories are influenced by self-reinforcing, contradictory and damaging forces. History affects network trajectory development, particularly with regard to financial resource access. The temporal processes reveal three contrasting classes of developmental trajectories for innovation networks.

The study methodology can account for theoretically derived factors leading to innovation, in and across types of networks and for changes over time; it moves beyond a cross-sectional approach. Although the model structure is generic, the parameters are based on a radical innovation, so the findings may not transfer directly.

Managers in innovation business networks can use the identified variables to improve network performance, by facilitating processes that inject financial capital and integrating heterogeneous skills that focus on a wider variety of skills that generate both exploratory and exploitative knowledge development.

This article contributes to discourses on network trajectories through an analysis of processes that influence the growth and decline of innovation business network performance. An original methodology generates and analyzes dynamic longitudinal network data.

This study investigates electronic customer service, e‐service, by Australian organisations, replicating and building on Heuchan et al.’s study of relationships among organisational characteristics and e‐service. Compared to one year earlier, the study found more organisations with Web sites, shorter response times to customer e‐mails and higher response quality to customer e‐mails. Response rate and response quality, however, was virtually the same – poor. Australian organisations have e‐service tools such as Web sites and e‐mail, yet they face an assimilation gap delivering e‐service. Organisational diffusion of innovations provides a theoretical base for these results and future research. The paper gives manager insights into existing e‐service and ways to improve e‐service in their organisation.