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University education in Nigeria has been in existence for over seven decades. The expansion and development are astronomical considering the number when the first university college was established. Currently, over 198 universities are spread across the length and breadth of the country. In spite of the expansion and development in this sector over time, the mode in which Nigerian scholars and researchers conduct research is often titled toward quantitative methods, thereby abandoning qualitative and mixed methods paradigms. Effort is geared toward establishing the reasons for the overrepresentation of quantitative paradigms in research space and the future of qualitative and mixed methods paradigms in Nigerian universities. A qualitative method was adopted in this study. In line with the approach, the interpretive paradigm is used to understand and make sense from the participants’ responses on the phenomenon. Narrative analysis is used to analyze the data. Findings indicated that old scholars and researchers are novices in qualitative and mixed methods research, that quantitative methods are scientific and the findings can be submitted to generalization, hence its overrepresentation. However, foreign-trained scholars and researchers are extending the future of these research approaches in Nigerian universities through the transfer of knowledge and skills.

Little is known about the extent to which management and engineering publications dedicated to the study of quality management topics make use of mixed methods, what types of studies have been conducted and how effective mixed methods have been. The aim of the current paper is to analyse how mixed methods have been used in quality management research.

To address this purpose, a bibliometric analysis was conducted of papers using mixed methods designs to investigate quality management issues and published in the SCOPUS database. CiteSpace software was used to assist in the categorisation and mapping process.

Ninety articles were identified and analysed. The results show that mixed methods are mainly used either to compare different perspectives drawn from quantitative and qualitative data or to develop better measurement instruments. Sequential mixes occur more often than concurrent approaches. Moreover, there is a link between the purpose of the study and the approaches followed to combine qualitative and quantitative methods. Yet, the contribution of the use of mixed methods to achieving the aims of the study is not easy to assess as the purposes of using mixed methods are often not clearly stated.

As one of the first papers to examine how qualitative and quantitative methods are being combined in quality management research, this study is expected to contribute to the literature by providing some insights into how mixed methods can be more effectively used in this field.

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.

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.

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 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.

The aim of this mixed methods study sought to investigate the preferences and challenges towards fourth industrial revolution (FIR) technologies adoption at hotels in Malaysia.

The study adopted partially-mixed concurrent quantitative-dominant mixed methods. The quantitative part of the study examined the preferred FIR technologies for hotels; whiles the qualitative part explored the challenges towards their adoption.

The results revealed that Internet of things, big data, cloud computing, artificial intelligence and cyber physical systems were the preferred FIR technologies for hotels. The findings revealed significant relationships between respondents' socio-demographic characteristics (age, gender, working experience, etc.) and preferred FIR technologies. Penultimately, challenges that affect the adoption of FIR technologies including investment cost, migration cost, trust, etc., were discussed.

In effect, findings from this study provide valuable information for policy makers (i.e. hotel owners, management, technology providers and future researchers). Most especially, the study unearths specific FIR technologies preferred by the hospitality and tourism industry. Also, the significant interplay of these preferences vis a vis respondents' socio-demographic characteristics, as well as challenges influencing their adoption also have major implications for policy makers. Another valuable contribution of this study lies in the research method utilised, since most studies on technology adoption either use quantitative or qualitative. These contributions for theory and practice have been thoroughly discussed.

Because the food service industry is more dependent on customer contact and human resources than other industries, it is crucial to understand the factors influencing employee job satisfaction to ensure that employees provide satisfactory service to customers. However, few studies have incorporated employee reviews of job portals into their research. Many job seekers tend to trust company reviews posted by employees on job portals based on the information provided by the company itself. Thus, this study utilized company reviews and job satisfaction ratings from employees in the food service industry on a job portal site, Job Planet, to conduct mixed-method research.

For qualitative research, we applied the Latent Dirichlet Allocation (LDA) model to food service industry company reviews to identify 10 job satisfaction factors considered important by employees. For quantitative research, four algorithms were used to predict job satisfaction ratings: regression tree, multilayer perceptron (MLP), random forest and XGBoost. Thus, we generated predictor variables for six cases using the probability values of topics and job satisfaction ratings on a five-point scale through LDA and used them to build prediction algorithms.

The analysis showed that algorithm accuracy performed differently in each of the six cases, and overall, factors such as work-life balance and work environment have a significant impact on predicting job satisfaction ratings.

This study is significant because its methodology and results suggest a new approach based on data analysis in the field of human resources, which can contribute to the operation and planning of corporate human resources management in the future.