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The purpose of this paper is to explore the nature of “researcher perspective” in articles published in the AIS Basket of 8 journals.

Purposive sampling: descriptive analysis of 659 articles published in three complete years of each of the eight leading journals (2001, 2008, 2015).

When observing phenomena, IS researchers mostly adopt the perspective of one of the stakeholders in the activities, commonly that of the sponsor of the information system that is in focus. 96% of relevant articles adopted a single-perspective approach, and 93% of those were oriented towards the system sponsor.

The discipline has not been exploiting opportunities to deliver greater value firstly through the adoption of perspectives other than that of the system sponsor, and secondly through dual- and multi-perspective research. Further, the ignoring of the viewpoints of other stakeholders is inconsistent with the requirements of the recently-adopted AIS Code of Ethics.

The dominance of single-perspective/system-sponsor-viewpoint research greatly constrains the benefits that IS research can deliver to IS practitioners and to the world at large.

The authors are not aware of any prior investigation into the nature of researcher perspective. We contend that an appreciation of the current bias is essential if IS research is to adapt, and thereby make far more useful contributions to practice.

The purpose of this paper is to improve the mechanical and tribological properties of Si₃N₄ ceramics and to make the application of Si₃N₄ ceramics as tribological materials more extensive.

Si₃N₄-based composite ceramics (SN-2L) containing nitrogen-doped graphene quantum dots (N-GQDs) were prepared by hot press sintering process through adding 2 Wt.% nanolignin as precursor to the Si₃N₄ matrix, and the dry friction and wear behaviors of Si₃N₄-based composite against TC4 disc were performed at the different loads by using pin-on-disc tester.

The friction coefficients and wear rates of SN-2L composite against TC4 were significantly lower than those of the single-phase Si₃N₄ against TC4 at the load range from 15 to 45 N. At higher load of 45 N, SN-2L/TC4 pair presented the lowest friction coefficient of 0.25, and the wear rates of the pins and discs were as low as 1.76 × 10⁻⁶ and 2.59 × 10⁻⁴mm³/N·m. The low friction and wear behavior could be attributed to the detachment of N-GQDs from the ceramic matrix to the worn surface at the load of 30 N or higher, and then an effective lubricating film containing N-GQDs, SiO₂, TiO₂ and Al₂SiO₅ formed in the worn surface. While, at the same test condition, the friction coefficient of the single-phase Si₃N₄ against TC4 was at a range from 0.45 to 0.58. The spalling and cracking morphology formed on the worn surface of single-phase Si3N4, and the wear mechanism was mainly dominated by adhesive and abrasive wear.

Overall, a high-performance green ceramic composite was prepared, and the composite had a good potential for application in engineering tribology fields (such as aerospace bearings).

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-05-2024-0161/

The purpose of this paper is to call researchers’ attention to cross-cultural research using online consumer reviews and multilingual textual analysis.

The authors discuss a selected literature review and the highlight of the four studies that show cross-cultural differences in online reviews on ethnic restaurants.

Applying multilingual textual analysis could prompt new venues to verify and expand future cross-cultural research in tourism and hospitality.

The paper introduces examples of multilingual textual analysis used for cross-cultural studies.

Reviews previous research on the links between business failures, macroeconomic conditions and insolvency law; and develops a mathematical, econometric model to investigate them further, using 1996‐1998 UK data. Presents and discusses the results, which suggest that the 1986 Insolvency Act did help to reduce the overall level of business failures and estimates that it saved 1100 companies from bankruptcy in the first three years after implementation. Finds that interest rates, price levels, levels of business formation, credit conditions and profit levels also affect business failure rates.

Near-fault pulse-like ground motions have distinct and very severe effects on reinforced concrete (RC) structures. However, there is a paucity of recorded data from Near-Fault Ground Motions (NFGMs), and thus forecasting the dynamic seismic response of structures, using conventional techniques, under such intense ground motions has remained a challenge.

The present study utilizes a 2D finite element model of an RC structure subjected to near-fault pulse-like ground motions with a focus on the storey drift ratio (SDR) as the key demand parameter. Five machine learning classifiers (MLCs), namely decision tree, k-nearest neighbor, random forest, support vector machine and Naïve Bayes classifier , were evaluated to classify the damage states of the RC structure.

The results such as confusion matrix, accuracy and mean square error indicate that the Naïve Bayes classifier model outperforms other MLCs with 80.0% accuracy. Furthermore, three MLC models with accuracy greater than 75% were trained using a voting classifier to enhance the performance score of the models. Finally, a sensitivity analysis was performed to evaluate the model's resilience and dependability.

The objective of the current study is to predict the nonlinear storey drift demand for low-rise RC structures using machine learning techniques, instead of labor-intensive nonlinear dynamic analysis.

This paper aims to investigate the tribological properties of polytetrafluoroethylene (PTFE) composites modified by nano-serpentine and nano-lanthanum oxide in a seawater environment.

In this paper, seven PTFE composites were prepared by unified design method and vacuum thermoforming method, and their hardness, water absorption and tribological properties were measured under seawater environment. The modification effects and thermal stability of the materials were analyzed by Fourier transform infrared spectroscopy, thermal gravimetry and differential scanning calorimetry. This paper analyzed the wear mechanism of PTFE composites by scanning electron microscopy and energy spectroscopy.

The results showed that the hardness of the PTFE composites were all improved, but the water absorption was increased with the increase of additives. The modification of nano-serpentine was successful and the thermal stability of PTFE composites was better. The lowest coefficient and minimum wear rate are 0.0267 and 8.67 × 10⁻⁵ · mm³ · (N · m)⁻¹ respectively, which is 34.9% and 76% less than the pure PTFE.

The analysis showed that the wear mechanism of PTFE composites was abrasive wear and a small amount of adhesive wear, and when the additive content was appropriate, it easily formed a transfer film on the surface mating parts.

Principal leadership is an important external environmental factor that affects and alleviates teachers' job burnout. The purpose of this paper is to explore the deep internal mechanisms of the influence of principal transformational leadership on teacher job burnout in the context of Chinese teachers.

A cluster sampling method was used to conduct a questionnaire survey on 990 elementary school teachers in 14 primary schools in Beijing. This study uses a structural equation model to analyze the chain intermediary effect of social-emotional competence and the student-teacher relationship between transformational leadership and teachers' job burnout.

The results reveal that transformational leadership has a significant negative predictive effect on teachers' job burnout; this kind of leadership affects teachers' job burnout through a chain intermediary effect of social and emotional competence and student-teacher relationship.

This research has discovered that teacher burnout is the result of the interaction of external environmental and individual internal factors. Transformational leadership, as an external environmental factor, positively predicts the internal social-emotional competence of the teacher, and then the teacher's internal social-emotional competence positively predicts the external student-teacher relationship. Finally, the teacher-student relationship of the external environment negatively predicts the job burnout of internal individual teachers.

The purpose of this study is to improve the dispersion of nanoserpentine modified with OA, KH550 and KH550/oleic acid in lubricating oil. The main aims are to analyze the influence of the dispersion and stability of nanoserpentine modified by different modifiers on the friction properties of lubricating oil.

The nanoserpentine particles obtained by ball-milling were modified by silane coupling agent KH550, OA and KH550/OA, respectively. The dispersity and stability of nanoserpentine in base lubricating oil were characterized by the absorbance value method, centrifuge precipitation rate method and static observation method. The MMU-5G screen display friction and wear tester was used to evaluate the tribological properties of C45E4/C45E4 friction pairs in corresponding lubricating oils. The surface morphology of the friction pairs was observed by scanning electron microscopy and energy dispersive spectroscopy.

The results showed that the dispersity and stability of nanoserpentine particles in lubricating oil were best modified by OA, followed by the KH550/OA and finally, the KH550. Nanoserpentine particles modified with oleic acid showed optimum tribological properties as lubricant additives.

This study can improve the dispersion stability of nanoserpentine particles in lubricating oil, increasing the antiwear and antifriction performance of lubricating oil, which has great significance in economic and military aspects.

The trend of “Deep Learning for Internet of Things (IoT)” has gained fresh momentum with enormous upcoming applications employing these models as their processing engine and Cloud as their resource giant. But this picture leads to underutilization of ever-increasing device pool of IoT that has already passed 15 billion mark in 2015. Thus, it is high time to explore a different approach to tackle this issue, keeping in view the characteristics and needs of the two fields. Processing at the Edge can boost applications with real-time deadlines while complementing security.

This review paper contributes towards three cardinal directions of research in the field of DL for IoT. The first section covers the categories of IoT devices and how Fog can aid in overcoming the underutilization of millions of devices, forming the realm of the things for IoT. The second direction handles the issue of immense computational requirements of DL models by uncovering specific compression techniques. An appropriate combination of these techniques, including regularization, quantization, and pruning, can aid in building an effective compression pipeline for establishing DL models for IoT use-cases. The third direction incorporates both these views and introduces a novel approach of parallelization for setting up a distributed systems view of DL for IoT.

DL models are growing deeper with every passing year. Well-coordinated distributed execution of such models using Fog displays a promising future for the IoT application realm. It is realized that a vertically partitioned compressed deep model can handle the trade-off between size, accuracy, communication overhead, bandwidth utilization, and latency but at the expense of an additionally considerable memory footprint. To reduce the memory budget, we propose to exploit Hashed Nets as potentially favorable candidates for distributed frameworks. However, the critical point between accuracy and size for such models needs further investigation.

To the best of our knowledge, no study has explored the inherent parallelism in deep neural network architectures for their efficient distribution over the Edge-Fog continuum. Besides covering techniques and frameworks that have tried to bring inference to the Edge, the review uncovers significant issues and possible future directions for endorsing deep models as processing engines for real-time IoT. The study is directed to both researchers and industrialists to take on various applications to the Edge for better user experience.

The increased interest among academicians to explore more about tax management behavior is evident in the literature on corporate tax avoidance. This paper aims to illustrate the multiple aspects that influence the tax avoidance behavior of corporations and its impacts through the systematic review method.

This study used “Tax Avoidance” OR “Tax Aggressiveness” OR “Tax Planning” as search strings to extract the relevant literature from the Scopus database. This study is a comprehensive analysis of existing literature on corporate tax avoidance behavior. Further, the keyword network analysis has been used to find out the most explored and dry research areas related to corporate tax avoidance behavior using VOSviewer software.

The study finds that taxation decision is an important managerial decision. Managers adopt tax avoidance tactics to boost postax profits to meet the shareholders’ expectations, particularly of risk-averse shareholders, and sometimes for their benefit also. With this, this study also finds that firms’ characteristics, political connections and corporate social responsibility activities also impact taxation decisions. In addition, the study identifies that tax-avoiding behavior has a contradictory impact on firm value, market growth and corporate transparency disclosure decisions.

The study assists the researchers by providing a brief overview of tax avoidance behavior, for corporates in understanding the implications of tax avoidance, and for policymakers to fix the taxation loopholes and bring necessary tax reforms.

This study adds to the existing literature by providing a thorough overview of theories, determinants and outcomes of corporate tax avoidance behavior.