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This bibliometric study provides an overview of research related to digital transformation (DT) in the tourism industry from 2013 to 2022. The goals of the research are as follows: (1) to identify the development of academic papers related to DT in the tourism industry, (2) to analyze dominant research topics and the development of research interest and research impact over time and (3) to analyze the change in research topics during the pandemic.

In this study, the authors processed 3,683 papers retrieved from the Web of Science and Scopus. The authors performed different types of bibliometric analyses to identify the development of papers related to DT in the tourism industry. To reveal latent topics, the authors implemented topic modeling based on latent Dirichlet allocation with Gibbs sampling.

The authors identified eight topics related to DT in the tourism industry: City and urban planning, Social media, Data analytics, Sustainable and economic development, Technology-based experience and interaction, Cultural heritage, Digital destination marketing and Smart tourism management. The authors also identified seven topics related to DT in the tourism industry during the Covid-19 pandemic; the largest ones are smart analytics, marketing strategies and sustainability.

To identify research topics and their development over time, the authors applied a novel methodological approach – a smart literature review. This machine learning approach is able to analyze a huge amount of documents. At the same time, it can also identify topics that would remain unrevealed by a standard bibliometric analysis.

The aim of this paper is to frame the stakeholder-driven system mapping approach in the context of climate change, building on stakeholder knowledge of system boundaries, key elements and interactions within a system and to introduce a decision support tool for managing and visualising this knowledge into insightful system maps with policy implications.

This methodological framework is based on the concepts of market maps. The process of eliciting and visualising expert knowledge is facilitated by means of a reference implementation in MATLAB, which allows for designing technological innovation systems models in either a structured or a visual format.

System mapping can contribute to evaluating systems for climate change by capturing knowledge of expert groups with regard to the dynamic interrelations between climate policy strategies and other system components, which may promote or hinder the desired transition to low carbon societies.

This study explores how system mapping addresses gaps in analytical tools and complements the systems of innovation framework. Knowledge elicitation, however, must be facilitated and build upon a structured framework such as technological innovation systems.

This approach can provide policymakers with significant insight into the strengths and weaknesses of current policy frameworks based on tacit knowledge embedded in stakeholders.

The developed methodological framework aims to include societal groups in the climate policy-making process by acknowledging stakeholders’ role in developing transition pathways. The system map codifies stakeholder input in a structured and transparent manner.

This is the first study that clearly defines the system mapping approach in the frame of climate policy and introduces the first dedicated software option for researchers and decision makers to use for implementing this methodology.

This study aims to uncover managerial representations of achieving competitive advantage in architectural practices operating within the United Kingdom (UK).

A sequential qualitative methodology is applied, underpinned by nine managerial interviews in five architectural practices, which are analysed using computer-assisted qualitative data analysis software.

In all, 108 representations are identified, with highly rated concepts including reputation, client satisfaction, fees and staff resources discussed in detail. The need for architectural practices to develop a competitive advantage within their sector is increasingly apparent, particularly during times of market turbulence. A total of 20 themes identified are clustered into four main groups focused on People (including Calibre of Staff, Attract Graduates; Qualified Staff); Product (including Emphasis on design, Specialisation, Competitive Fees): Process (including Low Overheads, Office Efficiency) and Potential (Reputation, Ability to Undertake Large Projects, Repeat clients, Ability to expand, Parent Company, Market Understanding and New Offices).

Despite numerous studies conducted on this subject, there has been no research to date documenting managerial representation on achieving competitive advantage in the context of architectural practices in the UK. This paper closes this gap in knowledge by contributing to underlying research on competitive advantage, focusing on the managerial representations within UK architectural practices.

This study aims to explore the motivations and business ideation processes of 776 entrepreneurs from three diverse cohorts of technology, youth and arts entrepreneurs.

Using an inductive approach inspired by grounded theory, observations resultant from the use of a Web-based digital test environment are openly coded, in which 776 individual entrepreneurs have stated their objectives for engaging in entrepreneurship and performed a business ideation process.

The study inductively derives a typology of objectives types – “GET GIVE MAKE LIVE” – and finds that beyond the pursuit of profitable opportunities, there is considerable variation, complexity and combinations to the reasons why individuals engage in entrepreneurship. A total of 76 percent of the population in this study have more than one objective, with 48% having more than one type of objective. While the arts entrepreneurs tended to engage in entrepreneurship to “LIVE” and the tech entrepreneurs were more inclined to “GET,” the most frequently observed objective type in all cohorts was to “MAKE.” A total of 74 percent of the entrepreneurs took an effectual approach and began defining their business idea with their core competency, yet technology entrepreneurs were the most likely to start by defining their key market.

Entrepreneurship educators, trainers and helpers should refrain from a standardized approach which assumes that entrepreneurs share the same set of singular motivations. Interventions might benefit from a student-centered program which promotes reflection and articulation of the entrepreneurs’ objectives and their diversity.

This study answers the call for research to embrace entrepreneurial diversity and compliment previous explorations of entrepreneurs’ motivations through an empirically grounded study of three diverse cohorts of entrepreneurs.

The study aims to summarise the literature on cancer care pathways at the diagnostic and treatment phases. The objectives are to find factors influencing the delivery of cancer care pathways; to highlight any interrelating factors; to find gaps in the literature concerning areas of research; to summarise the strategies and recommendations implemented in the studies.

The study used a qualitative approach and developed a causal loop diagram to summarise the current literature on cancer care pathways, from screening and diagnosis to treatment. A total of 46 papers was finally included in the analysis, which highlights the recurring themes in the literature.

The study highlights the myriad areas of research applied to cancer care pathways. Factors influencing the delivery of cancer care pathways were classified into different albeit interrelated themes. These include access barriers to care, hospital emergency admissions, fast track diagnostics, delay in diagnosis, waiting time to treatment and strategies to increase system efficiency.

As far as the authors know, this is the first study to present a visual representation of the complex relationship between factors influencing the delivery of cancer care pathways.

Surprisingly, most studies have failed to demonstrate a strong correlation between organizational constraints (conditions at work that make doing a job difficult) and job performance. The purpose of this paper is to challenge the view that constraints are a direct barrier on performance and take an alternative approach whereby constraints have an indirect effect via decreased motivation and increased workload. Further, differential effects of various constraints are examined.

Qualitative and quantitative data were collected from 660 engineers licensed in the state of Florida using a single online survey.

Qualitative results showed that the most commonly experienced constraints were from coworkers and organizational rules and procedures. Constraints identified as having a greater detrimental effect on motivation are from the supervisor, and organizational rules and procedures. Quantitative results supported an indirect effects model that includes an indirect path via motivation, and a path via workload, which had a curvilinear component.

This is one of few studies to explain the relationship between constraints and performance, rather than simply estimate it. The use of mixed methods allows us to gain an in-depth understanding of constraints, and the convergence of findings across the methods increases confidence in this study’s results.

This study aims to explore and empirically test variables influencing material delivery schedule inaccuracies?

A mixed-method case approach is applied. Explanatory variables are identified from the literature and explored in a qualitative analysis at an automotive original equipment manufacturer. Using logistic regression and random forest classification models, quantitative data (historical schedule transactions and internal data) enables the testing of the predictive difference of variables under various planning horizons and inaccuracy levels.

The effects on delivery schedule inaccuracies are contingent on a decoupling point, and a variable may have a combined amplifying (complexity generating) and stabilizing (complexity absorbing) moderating effect. Product complexity variables are significant regardless of the time horizon, and the item’s order life cycle is a significant variable with predictive differences that vary. Decoupling management is identified as a mechanism for generating complexity absorption capabilities contributing to delivery schedule accuracy.

The findings provide guidelines for exploring and finding patterns in specific variables to improve material delivery schedule inaccuracies and input into predictive forecasting models.

The findings contribute to explaining material delivery schedule variations, identifying potential root causes and moderators, empirically testing and validating effects and conceptualizing features that cause and moderate inaccuracies in relation to decoupling management and complexity theory literature?

This paper aims to describe an interdisciplinary and innovative research conducted in Switzerland, at the Geneva School of Business Administration HES-SO and supported by the State Archives of Neuchâtel (Office des archives de l'État de Neuchâtel, OAEN). The problem to be addressed is one of the most classical ones: how to extract and discriminate relevant data in a huge amount of diversified and complex data record formats and contents. The goal of this study is to provide a framework and a proof of concept for a software that helps taking defensible decisions on the retention and disposal of records and data proposed to the OAEN. For this purpose, the authors designed two axes: the archival axis, to propose archival metrics for the appraisal of structured and unstructured data, and the data mining axis to propose algorithmic methods as complementary or/and additional metrics for the appraisal process.

Based on two axes, this exploratory study designs and tests the feasibility of archival metrics that are paired to data mining metrics, to advance, as much as possible, the digital appraisal process in a systematic or even automatic way. Under Axis 1, the authors have initiated three steps: first, the design of a conceptual framework to records data appraisal with a detailed three-dimensional approach (trustworthiness, exploitability, representativeness). In addition, the authors defined the main principles and postulates to guide the operationalization of the conceptual dimensions. Second, the operationalization proposed metrics expressed in terms of variables supported by a quantitative method for their measurement and scoring. Third, the authors shared this conceptual framework proposing the dimensions and operationalized variables (metrics) with experienced professionals to validate them. The expert’s feedback finally gave the authors an idea on: the relevance and the feasibility of these metrics. Those two aspects may demonstrate the acceptability of such method in a real-life archival practice. In parallel, Axis 2 proposes functionalities to cover not only macro analysis for data but also the algorithmic methods to enable the computation of digital archival and data mining metrics. Based on that, three use cases were proposed to imagine plausible and illustrative scenarios for the application of such a solution.

The main results demonstrate the feasibility of measuring the value of data and records with a reproducible method. More specifically, for Axis 1, the authors applied the metrics in a flexible and modular way. The authors defined also the main principles needed to enable computational scoring method. The results obtained through the expert’s consultation on the relevance of 42 metrics indicate an acceptance rate above 80%. In addition, the results show that 60% of all metrics can be automated. Regarding Axis 2, 33 functionalities were developed and proposed under six main types: macro analysis, microanalysis, statistics, retrieval, administration and, finally, the decision modeling and machine learning. The relevance of metrics and functionalities is based on the theoretical validity and computational character of their method. These results are largely satisfactory and promising.

This study offers a valuable aid to improve the validity and performance of archival appraisal processes and decision-making. Transferability and applicability of these archival and data mining metrics could be considered for other types of data. An adaptation of this method and its metrics could be tested on research data, medical data or banking data.