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Mixed methods research is useful to enhance theoretical and practical research contributions. However, single methods have predominated much logistics and supply chain management (LSCM) research. This paper presents a review of mixed methods research across ten years in LSCM to determine their usage, identify benefits and inhibitors, and provide suggestions for LSCM researchers to realise the benefits from using mixed methods.

This paper adopts a mixed methods approach through a quantitative analysis of methods used in six leading LSCM journals, an e-mail survey of mixed methods article authors during the review period, and four published case studies that used mixed methods.

Only 144 (ten percent) of all empirical articles were published using mixed methods during the review period. A range of benefits and inhibitors regarding mixed methods adoption were found. Suggestions for LSCM authors include research training in mixed methods use and developing a project-specific research design due to the specificity and complexity associated with mixed methods research.

LSCM is at a critical juncture, shaped by new contexts, themes and challenges, and would benefit from different research approaches and methods. This paper contributes to the LSCM domain through analysing the current state, benefits and inhibitors of mixed methods research in LSCM journals to provide a renewed call to action and guidelines for mixed methods LSCM research, and suggesting research design adaptation to enable agile and resilient research when investigating rapidly changing and complex phenomena.

The purpose of this paper is to examine the currently known techniques to tackle money laundering in Bitcoin mixers, and examine what gaps exist that would allow a criminal to get away with laundering Bitcoin obtained through illicit activities.

This paper first establishes the relevant properties of Bitcoin, how transactions occur over the Bitcoin network and then introduces the Bitcoin transaction graph as an important data structure for any analysis of Bitcoin transactions. Next, the paper outlines how Bitcoin mixing works, along with the relevant properties of mixers that would be relevant for money laundering. The paper then assesses the known methods for identifying mixed transactions within the Bitcoin network, followed by an assessment on identifying money laundering activities on known mixed transactions.

This paper argues that there remains a gap for criminals to launder money through Bitcoin mixing services as known methods would unlikely be able to trace a tainted transaction that goes through a decentralized mixer that uses off-chain communication techniques to coordinate the mixing and charges randomized mixing fees.

The study of known methods is restricted to literature published in the public domain. There are private organizations that are tackling similar problems, but their methods are not published and therefore cannot be included in this paper.

To best of the author’s knowledge, this is the first paper that performs a contemporaneous review on anti-money laundering in the context of Bitcoin mixing. This paper could assist regulators and policymakers in their understanding of Bitcoin mixers and provide guidance on where they should focus their resources to address the money laundering problem of Bitcoin mixing.

This study aims to investigate NiTi alloys’ characterization and corrosion behaviour produced by two different powder metallurgy (PM) methods.

It was pre-formed under a protective atmosphere at 900 °C under a force of 45 MPa and sintered for 1 h under 10–6 Mbar in an atmosphere-controlled heat treatment furnace at 1,100 °C. The relationship between microstructural properties, SEM, XRD, density, microhardness and corrosion behaviour of pre-alloyed NiTi alloys produced by two different methods with the production method was investigated.

As a result of the studies, TiO, NiTi, NiTi2 and Ni3Ti intermetallics were determined in XRD examinations. The best surface roughness was observed in the mechanically milled (MM’ed) pre-alloyed NiTi alloy compared to the pre-alloyed NiTi alloy mixed with turbula. The corrosion tests performed in 3.5% NaCl solution determined that the MM’ed pre-alloyed NiTi alloy had better corrosion resistance than the pre-alloyed NiTi alloy mixed with turbula. Pitting corrosion was visualized in the SEM images taken from the corrosion surfaces.

Two different PM methods produced pre-alloyed NiTi powders, and the effects of these methods on the mechanical and corrosion resistance of NiTi alloys were systematically investigated for the first time.

This study aims to describe the development of hospitality research in terms of research methods and data sources used in the 2010s.

Content analyses of the research methods and data sources used in original hospitality research published in the 2010s in the Cornell Hospitality Quarterly (CQ), International Journal of Hospitality Management (IJHM), International Journal of Contemporary Hospitality Management (IJCHM), Journal of Hospitality and Tourism Research (JHTR) and International Hospitality Review (IHR) were conducted. It describes whether the time span, functional areas and geographic regions of data sources were related to the research methods and data sources.

Results from 2,759 original hospitality empirical articles showed that marketing research used various research methods and data sources. Most finance articles used archival data, while most human resources articles used survey designs with organizational data. In addition, only a small amount of research used data from Oceania, Africa and Latin America.

This study sheds some light on the development of hospitality research in terms of research method and data source usage. However, it only focused on five English-based journals from 2010–2019. Therefore, future studies may seek to understand the impact of the COVID-19 pandemic on research methods and data source usage in hospitality research.

This is the first study to examine five hospitality journals' research methods and data sources used in the last decade. It sheds light on the development of hospitality research in the previous decade and identifies new hospitality research avenues.

The lockdown of schools to control the spread of Coronavirus Disease 2019 (COVID-19) has adversely affected elementary and secondary school students in China. However, little evidence is available that documents their perceptions toward the use of e-learning under the pandemic context. This study aims to explore (1) the underlying determinants influencing the students' intentions to continue e-learning and (2) the role that the COVID-19 pandemic plays in this decision. This study extends the expectation–confirmation model (ECM) through a mixed-methods study.

This study uses a mixed-methods approach. Phase 1 draws conclusions and extracts significant elements related to e-learning during the pandemic by analyzing students' user reviews from platforms. Based on the findings, the authors identify the relevant variables and develop an extended ECM by integrating attitudes and the perceived threat of COVID-19. In Phase 2, the research model is empirically validated using data collected from 36,920 elementary and secondary school students in China. This study uses a mixed-methods approach. Phase 1 draws conclusions and extracts significant elements related to e-learning during the pandemic by analyzing students' user reviews from platforms. Based on the findings, the authors identify the relevant variables and develop an extended ECM by integrating attitudes and the perceived threat of COVID-19. In Phase 2, the research model is empirically validated using data collected from 36,920 elementary and secondary school students in China.

In Phase 1, the results reveal that students' most concern elements in the COVID-19 pandemic are user perceptions toward e-learning and threat perceptions of COVID-19. In Phase 2, the results reveal that perceived usefulness and attitude are the most significant factors in students' intention to continue. The perceived threat of COVID-19 indirectly influences their intention to continue.

The study extends the ECM with a mixed-methods study to understand students' perceptions toward e-learning during the COVID-19 pandemic. This study can be helpful in related studies in the context of COVID-19-related diseases or other novel scenarios.

This study aims to test the complaints of tourists who visit five-star hotels in Antalya, Turkey, on those same hotels’ websites.

In the study, the data were collected with qualitative methods but analyzed with the mixed analysis method. In this context, the authors collected 1,012 comments on the website between 2016 and 2019.

According to the results of the study, the most intense complaints were found to be concentrated in categories such as ambience, food and staff.

First of all, it is thought that it will make an important contribution to the literature, since different methodologies are adopted in the study. In addition, online shares, evaluations and comments produce positive or negative results for the destination or business in question. It is necessary to closely monitor such activities in electronic environments, as they may have negative consequences, thus revealing the need to take corrective or preventive measures. For this reason, the research is important in terms of not having such a large-scale study in the literature and contributing to the hospitality industry.

This research intends to develop a model for predicting employees at a high-risk attrition and identify the most important factors affecting them.

In this study, using the triangulation technique of a mixed research method, the employee attrition problem is investigated by identifying its affecting factors. For that matter, data related to the human resources department of a pharmaceutical company in Iran are used. And to achieve the intended goal, advanced data mining algorithms and interviews with human resource managers are applied.

A model for predicting employees at a high-risk attrition is presented based on the gradient boosting machine algorithm with 89% accuracy. The use of the mixed research approach shows that qualitative and quantitative methods can be more effective in identifying the factors affecting employee churn or loss of staff. The results also contain a new situation arising out of the COVID-19 pandemic and remote working scenarios having impact on employee attrition. Finally, human resource policies are presented based on variables related to each of the identified factors.

The novel contributions of this study include real data related to a leading pharmaceutical company as well as a combination of two quantitative and qualitative methods. The hybrid approach can identify the reasons for attrition and, consequently, retention policies to benefit from the advantage of both approaches. Data mining can be useful to identify the factors, which are usually not mentioned in termination interviews, such as direct managers. On the other hand, the results obtained from termination interviews can also include features that the authors cannot identify through data mining, which are specifically related to the characteristics of the pharmaceutical industry such as building a more professional career path. From a practical perspective, since this company specializes in pharmaceutical marketing in a new way and is primarily comprised graduates, it is important to note that the churn of specialized people disperses organizational and technological know-how. On the other hand, the pharmacist community in Iran is small, and their attrition might adversely affect not only the reputation of an organization but the employer's brand as well. So, this research would help other similar firms in retaining their valuable human capital.

Fused Filament Fabrication (FFF) is an extrusion-based manufacturing process using fused thermoplastics. Despite its low cost, the FFF is not extensively used in high-value industrial sectors mainly due to parts' anisotropy (related to the deposition strategy) and residual stresses (caused by successive heating cycles). Thus, this study aims to investigate the process improvement and the optimization of the printed parts.

In this work, a meshless technique – the Radial Point Interpolation Method (RPIM) – is used to numerically simulate the viscoplastic extrusion process – the initial phase of the FFF. Unlike the FEM, in meshless methods, there is no pre-established relationship between the nodes so the nodal mesh will not face mesh distortions and the discretization can easily be modified by adding or removing nodes from the initial nodal mesh. The accuracy of the obtained results highlights the importance of using meshless techniques in this field.

Meshless methods show particular relevance in this topic since the nodes can be distributed to match the layer-by-layer growing condition of the printing process.

Using the flow formulation combined with the heat transfer formulation presented here for the first time within an in-house RPIM code, an algorithm is proposed, implemented and validated for benchmark examples.

Conducting mixed methods (MM) research is one of many ways to study a research topic in the hospitality and tourism domain. This paper aims to offer a synopsis and guidelines on the application of MM research design within hospitality and tourism.

This paper summarizes the pivotal information on how to write high-quality MM research papers. In doing so, the paper draws on common practices and advice from previous books and articles as well as advice from the editor-in-chief and one of the associate editors of the International Journal of Contemporary Hospitality Management.

This study provides explanations, guidelines and examples of MM research design.

It offers specific recommendations and examples of MM research design in the hospitality and tourism field. This study concludes with a discussion of common mistakes, useful tips and a framework for the self-assessment of MM research.

This is one of the few studies providing specific guidance and recommendations on designing MM research papers in hospitality and tourism studies.

In this article, we discuss an innovative audience research methodology developed for the AHRC-funded “Beyond the Multiplex: Audiences for Specialised Film in English Regions” project (BtM). The project combines a computational ontology with a mixed-methods approach drawn from both the social sciences and the humanities, enabling research to be conducted both at scale and in depth, producing complex relational analyses of audiences. BtM aims to understand how we might enable a wide range of audiences to participate in a more diverse film culture, and embrace the wealth of films beyond the mainstream in order to optimise the cultural value of engaging with less familiar films. BtM collects data through a three-wave survey of film audience members’ practices, semi-structured interviews and film-elicitation groups with audience members alongside interviews with policy and industry experts, and analyses of key policy and industry documents. Bringing each of these datasets together within our ontology enables us to map relationships between them across a variety of different concerns. For instance, how cultural engagement in general relates to engagement with specialised films; how different audiences access and/or share films across different platforms and venues; how their engagement with those films enables them to make meaning and generate value; and how all of this is shaped by national and regional policy, film industry practices, and the decisions of cultural intermediaries across the fields of film production, distribution and exhibition. Alongside our analyses, the ontology enables us to produce data visualisations and a suite of analytical tools for audience development studies that stakeholders can use, ensuring the research has impact beyond the academy. This paper sets out our methodology for developing the BtM ontology, so that others may adapt it and develop their own ontologies from mixed-methods empirical data in their studies of other knowledge domains.