Free sample articles newly available from International Journal of Corporate Governance

The following sample articles from the International Journal of Corporate Governance are now available here for free:

• The relationship between the socio-emotional wealth dimensions and earnings management by thresholds: evidence from French family companies
• India-specific corporate social responsibility-consumer perception scale
• Corporate governance mechanisms and R&D intensity in OECD countries
• Executive compensation structure and earnings management: evidence from Australian listed firms in the period of governance reform

Platforming startups

Startups face a major challenge in trying to home in on open innovation partners. Research in the International Journal of Entrepreneurship and Innovation Management, looks at the various ways in which startups search for such partners, the platforms they use to find connections, and the advantages and limitations of those platforms.

Izabella Bereczki and Johann Füller of the University of Innsbruck in Austria, and Oana Stănculescu of the Transylvanian Furniture Cluster in Cluj-Napoca, Romania, point out that previous studies have tended to focus on corporate entities and SMEs (small and medium-sized enterprises), startups have not been the subject of research in this context to any significant degree so far. Indeed, previous researchers have highlighted the fact that there is a need to remedy this situation.

Startups it can be said are often highly innovative. Indeed, the very nature of such companies is usually underpinned by an entirely novel idea aimed at fulfilling an unrequited need. However, they, by definition might lack experience and certainly rarely have substantial resources at their disposal. Generally, the researchers say, firms can innovate with customers, suppliers, universities, research institutions or even competitors in order to foster innovation. Seeking out these partners is time-consuming and can be costly. Manual approaches are not always effective nor efficient and so many startups might turn to digital platforms that facilitate networking between those who have and those who have not, as it were.

“We believe open innovation is fundamental to innovation effectiveness because it promotes innovation and a cooperation culture,” the team writes. “Open business models can be used by start-ups to create and capture value through systematic collaboration with external partners.” The team points out that the platforms at present focus on corporations who want to search for partners. The converse search is not catered for so well and is therefore an opportunity in the making for the platforms that could ultimately be beneficial to all parties including startups, which are currently missing out.

Bereczki, I., Füller, J. and Stanculescu, O. (2022) ‘The perfect match! Open innovation platforms – assets for collaborative start-ups’, Int. J. Entrepreneurship and Innovation Management, Vol. 26, Nos. 3/4, pp.133–160.

Free open access article available: "A multicriteria decision support approach for evaluating highly complex adaptive reuse plans"

The following paper, "A multicriteria decision support approach for evaluating highly complex adaptive reuse plans" (International Journal of Multicriteria Decision Making 9(1) 2022), is freely available for download as an open access article.

It can be downloaded via the full-text link available here.

Special issue published: "Advances in Sustainable Mobility and Automotive Industry in South America"

International Journal of Automotive Technology and Management 22(3) 2022

• Institutional framework and the advance of electromobility: the case of South America
• End-of-life electric vehicles batteries in Brazil: modelling ways after the first use
• Artificial intelligence as a determinant for reshaping the automotive industry and urban mobility services
• Industrial public policies and open innovation in Brazil: proposal of a performance measurement system at Fiat Chrysler Automobiles
• The transition to electric mobility: opportunities for the automotive value chain in Argentina

Free sample articles newly available from International Journal of Computational Biology and Drug Design

The following sample articles from the International Journal of Computational Biology and Drug Design are now available here for free:

• A GPU based virtual screening tool using SOM
• Molecular docking, ADME and toxicity study of some chemical and natural plant based drugs against COVID-19 main protease
• Pharmacokinetic and molecular docking studies of natural plant compounds of Hibiscus sabdariffa to design antihypertensive compounds targeting AT2R Bhanu Sharma
• Ultrasonic-assisted rapid extraction of Cassia sieberiana D.C.: a Box-Behnken design process optimisation
• Molecular docking studies of Staphylococcal clumping factor A inhibitors from Elettaria cardamomum and Acacia nilotica

Special issue published: "Machine Learning for Energy Efficient Embedded and Computing Systems"

International Journal of Embedded Systems 15(3) 2022

• Computer embedded automatic test system based on VxWorks
• Virtual sports rehabilitation and monitoring system for the elderly based on intelligent interaction and embedded system
• Digital medical instrument based on embedded computer system
• Embedded system for mobile interconnection control system of sports training cyclists
• National cybersecurity: assessment, risks and trends
• Development of methods formalisation subject technology design of multimedia edition
• Physical fitness evaluation system for athlete selection based on big data technology
• Beidou GPS SINS satellite positioning system based on embedded operating system
• A simple measurement matrix for compressed sensing of synthetic aperture ultrasound imaging
• Data mining in college student education management information system

Research pick: Machine learning for pharma - "Machine learning methods for predicting the biological activities of molecules in high diverse databases"

There are literally millions of known chemical compounds. Huge numbers of these substances are used in industry, in agriculture, in the home, as medicines, and in countless other applications. Finding novel compounds with specific properties, such as a new pharmaceutical with fewer side effects than the old one, is a major focus of many research teams around the world. Often, software is used to scan databases of known chemicals but can also be used to predict the properties of previously unknown substances that might be synthesised in a laboratory should those properties fit the brief.

Now, writing in the International Journal of Information and Communication Technology, Faisal Saeed of the College of Computer Science and Engineering at Taibah University in Medina, Saudi Arabia, explains that predicting the characteristics of a new molecular structure in silico, in the computer, in other words, still presents many major challenges to drug discovery teams. In his paper, Saeed, suggests that machine learning might open wide the bottleneck by finding new ways to identify novel substances with particular physiological properties that might make them useful as new pharmaceuticals for a wide range of diseases and conditions.

Saeed has demonstrated that a combined effort might work well. He has tested different machine learning methods on diverse molecular datasets, including naïve Bayes, sequential minimal optimisation, Bayesian network, decision tree, support vector machine, K-nearest neighbours, random tree, and reduced error pruning, REPTree. The tests used different combinations of base classifiers to assess how well they would work against different types of dataset.

The K-nearest neighbour (KNN) approach, Saeed found, works far better than any other approach. Moreover, the ensemble learning method Adaboost (KNN) was the most effective of the KNN approaches. The downside is that this type of base classifier approach requires a lot of computer power to process a diverse dataset and to predict the biological activity of the molecules in that dataset. It might be possible in the next step to improve efficiency and reduce computing costs by adding a pre-processing step before the intensive analysis of the dataset is carried out.

Saeed, F. (2022) ‘Machine learning methods for predicting the biological activities of molecules in high diverse databases’, Int. J. Information and Communication Technology, Vol. 21, No. 2, pp.170–180.

Research pick: Managing the climate disaster - "Examining the transition of natural disaster management for climate change"

Researchers in South Korea discuss how we must adapt our approaches to disaster management to help us cope with the potentially devastating effects of climate change. Writing in the International Journal of Business Continuity and Risk Management, the team uses qualitative content analysis to describe and analyse the three levels of natural disaster management. These three levels – international, national, and local – are considered in the context of three proposed stages of climate change – before climate change, the first half, and the second half.

Kyong-Jin Park of Daegu Haany University in Gyeongsan City, Bong-Woo Lee of Seoul Digital University in Seoul, and Kyoo-Man Ha of Inje University in Gimhae City explain that all “stakeholders” the world over need to address international cooperation, sustainability, education, and training for survival. Their work suggests a history and a chronology where management from 1951 to 1990 was provincial, the period 1991 to 2040 will be seen as patriotic, but the period 2041 to 2100 will be the period of survival-oriented management.

The team alludes to the fact that while there may be denialism and ignorance about climate change, the truth is out there. Moreover, given that we are all culpable, we must all now play our role in the disaster management that is needed if we are to mitigate the impact of climate change on our lives, the lives of future generations, and indeed the future of life on earth. There is an ethical obligation on all of us and on all governments.

As they say, there is no Planet B, we have to work to protect and fix this one before it is too late. Lifestyle changes must take place from the local up to the national and then international level. Climate change is not a natural disaster but it will be disastrous for nature and ourselves unless we have the collective will to address the problems and manage them.

Park, K-J., Lee, B-W. and Ha, K-M. (2022) ‘Examining the transition of natural disaster management for climate change’, Int. J. Business Continuity and Risk Management, Vol. 12, No. 2, pp.116–130.

Research pick: "Privacy preserving data mining – past and present"

A survey of privacy-preserving data-mining techniques published in the International Journal of Business Intelligence and Data Mining assesses the pros and cons of each approach and offers guidance to potential users.

G. Sathish Kumar of the Department of Computer Science and Engineering at the Sri Krishna College of Engineering and Technology in Coimbatore and K. Premalatha of the Department of Computer Science and Engineering at the Bannari Amman Institute of Technology in Erode, both in Tamil Nadu, India explain how data mining has come to the fore as a powerful way to find patterns and correlations in big data.

However, as with any useful tool it can be mishandled or abused. In the case of big data, there are risks associated with breaches of private and personal information. This is particularly important given that data mining is so widely used with disparate data sets including criminal records, consumer shopping habits, bank transactions, medical information, and much more. Third parties might gain access to the identity of individuals represented in a database and so see associated information regarding that kind of personal and private data. A total breach would represent the worst-case scenario where all information and all individuals in a database is revealed to that third party.

There is therefore a pressing need to have full control of the data being mined so that third parties, malicious or otherwise, cannot compromise that data. The team has reviewed the various approaches and describes the benefits and disadvantages of each, including randomisation, anonymisation, condensation, cryptographic, fuzzy, and statistical methods of privacy preservation in data mining.

It is inevitable that there is always compromise in any approach. Indeed, the team has found that no technique outperforms all the others in all measures. Some work better than others in a given situation but there are trade-offs with each, the team writes. As such, there is still a need, despite recent advances in this area, to develop a system that can solidly preserve privacy while allowing data mining to be carried out.

Kumar, G.S. and Premalatha, K. (2022) ‘Privacy preserving data mining – past and present’, Int. J. Business Intelligence and Data Mining, Vol. 21, No. 2, pp.149–170.

Research pick: Algorithmic pest control - "Rapid detection and identification of major vegetable pests based on machine learning"

Machine learning has now been used to identify important pests that can ravage vegetable crops, according to work published in the International Journal of Wireless and Mobile Computing.

Changzhen Zhang of Kaili University in Guizhou, Yaowen Ye, Deqin Xiao, Long Qi, and Jianjun Yin of the South China Agricultural University in Guangzhou, China point out that effective pest control requires knowledge of the species affecting the plants and the level of infestation. The team has used a so-called “bag-of-features” model to develop an automatic pest monitoring system has been. They explain that their approach combines remote information processing technology and machine vision technology.

The proposed system can be implemented in a vegetable crop field to monitor four major pests: Phyllotreta striolata (the Striped Flea Beetle, a pest of brassicas), Frankliniella occidentalis (the invasive Western Flower Thrips, feeds on some 500 or more different species of vegetable, fruit, and flower), Bemisia tabaci (the Tobacco White Fly, which affects tomato and other related plants), and Plutella xylostella (the diamond-back moth, a pest of cruciform crops).

The team demonstrated an error rate of less than 10 percent when compared with detection and counting by people trained to spot the pests. Given that B. tabaci can reduce tomato crop yields by 60 percent so the detection of such species is critical to efficient and effective farming. The other species mentioned can all affect a wide variety of crops with devastating consequences when infestation is allowed to run rampant.

The team has demonstrated success in a controlled environment. The next step will be to test the system and improve its abilities in a more complex and realistic vegetable-growing environment.

Zhang, C., Ye, Y., Xiao, D., Qi, L. and Yin, J. (2022) ‘Rapid detection and identification of major vegetable pests based on machine learning’, Int. J. Wireless and Mobile Computing, Vol. 22, Nos. 3/4, pp.223–235.

Research pick: The daily grind of the rumour mill - "Machine learning classifiers with pre-processing techniques for rumour detection on social media: an empirical study"

Research published in the International Journal of Cloud Computing looks at how machine learning might allow us to analyse the nature and characteristics of social media updates and detect which of those updates are adding grist to the rumour mill rather than being factual.

Fake news has been with us ever since the first gossip passed on a rumour back in the day. But, with the advent of social media, it is now so much easier to spread fake news, disinformation, and propaganda to a vast global audience with little constraint. A rumour can make or break a reputation. These days, that might happen the world over through the amplifying echo chamber of social media.

Mohammed Al-Sarem, Muna Al-Harby, Faisal Saeed, and Essa Abdullah Hezzam of Taibah University in Medina, Saudi Arabia have surveyed the different text pre-processing approaches for approaching the vast quantities of data that pour from social media on a daily basis. How well these approaches work in the subsequent rumour detection analysis is critical to how well fake news can be spotted and stopped. The team has tested various approaches on a dataset of political news-related tweets from Saudi Arabia.

Pre-processing can look at the three most relevant characteristics of an update before the text analysis is carried out and silo the different updates accordingly: First, it can look at the use of question marks and exclamation marks and the word count. Secondly, it can look at whether an account is verified or has properties more often associated with a fake or bot account, such as tweet count, replies, retweets, etc. Thirdly, it can look at user-based features, such as the user name and the user’s logo or profile picture.

The researchers found that pre-processing can improve analysis significantly when the output is fed to any of support vector machine (SVM), multinomial naïve Bayes (MNB), and K-nearest neighbour (KNN) classifiers. However, those classifiers do react differently depending on what combination of pre-processing techniques is used. For instance, removing stop words, and cleaning out coding tags, such as HTML, stemming, and tokenization.

Al-Sarem, M., Al-Harby, M., Saeed, F. and Hezzam, E.A. (2022) ‘Machine learning classifiers with pre-processing techniques for rumour detection on social media: an empirical study’, Int. J. Cloud Computing, Vol. 11, No. 4, pp.330–344.

Research pick: Finding at-risk students - "Detecting students at risk using machine learning: applications to business education"

Traditionally, attendance and exam results have been the main way in which educators can show whether or not a student is struggling with the course. This is done retrospectively. With the advent of cloud-based learning technology and online courses, especially during the COVID-19 pandemic, these metrics are not necessarily the best way to catch at-risk students so that they can be helped.

The converse of that is that this technology can be used to provide and analyse useful data about the students, which can itself highlight those that might be struggling more quickly than can conventional assessment. Moreover, it can do this in a much more timely manner than a retrospective look at attendance and infrequent exam results.

Owen P. Hall Jr. of the Graziadio Business School at Pepperdine University in Malibu, California, USA, describes a machine-learning approach to detecting at-risk students in the International Journal of Social Media and Interactive Learning Environments. “At-risk” is a three-pronged definition alluding to whether a student is considering leaving a course, whether the institution is planning to end the student’s place on the course, or whether they are in a probationary period because of problems they have faced or concerns their teachers have about their course work, attendance, and results.

Machine learning has been used to predict examination grades and even attendance in some educational settings for many years. It is also commonly used to group students for study classes and other activities. It has even been used to detect cheating and plagiarism. It is perhaps therefore not such a great leap to picture the use of machine learning in helping students in another way.

Hall suggests that the machine-learning approach can analyse all the data associated with a student, almost continuously, and determine early on whether a student is at-risk or on the verge of being in that position. At this point, teachers and tutors might intervene to help without delay. The lack of delay to the assistance they give will tend to lead to a better outcome for such students.

“Engaging faculty, educational researchers, and administration in the risk mitigation paradigm is essential for ensuring student success,” writes Hall. Machine learning offers a novel tool to help with this process, improve student outcomes, and reduce dropout rates in an increasingly pressured educational system.

Hall Jr., O.P. (2022) ‘Detecting students at risk using machine learning: applications to business education’, Int. J. Social Media and Interactive Learning Environments, Vol. 6, No. 4, pp.267–289.