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Prediction is a critical task in targeted online advertising, where predictions better than random guessing can translate to real economic return. This study aims to use machine learning (ML) methods to identify individuals who respond well to certain linguistic styles/persuasion techniques based on Aristotle’s means of persuasion, rhetorical devices, cognitive theories and Cialdini’s principles, given their psychometric profile.

A total of 1,022 individuals took part in the survey; participants were asked to fill out the ten item personality measure questionnaire to capture personality traits and the dysfunctional attitude scale (DAS) to measure dysfunctional beliefs and cognitive vulnerabilities. ML classification models using participant profiling information as input were developed to predict the extent to which an individual was influenced by statements that contained different linguistic styles/persuasion techniques. Several ML algorithms were used including support vector machine, LightGBM and Auto-Sklearn to predict the effect of each technique given each individual’s profile (personality, belief system and demographic data).

The findings highlight the importance of incorporating emotion-based variables as model input in predicting the influence of textual statements with embedded persuasion techniques. Across all investigated models, the influence effect could be predicted with an accuracy ranging 53%–70%, indicating the importance of testing multiple ML algorithms in the development of a persuasive communication (PC) system. The classification ability of models was highest when predicting the response to statements using rhetorical devices and flattery persuasion techniques. Contrastingly, techniques such as authority or social proof were less predictable. Adding DAS scale features improved model performance, suggesting they may be important in modelling persuasion.

In this study, the survey was limited to English-speaking countries and largely Western society values. More work is needed to ascertain the efficacy of models for other populations, cultures and languages. Most PC efforts are targeted at groups such as users, clients, shoppers and voters with this study in the communication context of education – further research is required to explore the capability of predictive ML models in other contexts. Finally, long self-reported psychological questionnaires may not be suitable for real-world deployment and could be subject to bias, thus a simpler method needs to be devised to gather user profile data such as using a subset of the most predictive features.

The findings of this study indicate that leveraging richer profiling data in conjunction with ML approaches may assist in the development of enhanced persuasive systems. There are many applications such as online apps, digital advertising, recommendation systems, chatbots and e-commerce platforms which can benefit from integrating persuasion communication systems that tailor messaging to the individual – potentially translating into higher economic returns.

This study integrates sets of features that have heretofore not been used together in developing ML-based predictive models of PC. DAS scale data, which relate to dysfunctional beliefs and cognitive vulnerabilities, were assessed for their importance in identifying effective persuasion techniques. Additionally, the work compares a range of persuasion techniques that thus far have only been studied separately. This study also demonstrates the application of various ML methods in predicting the influence of linguistic styles/persuasion techniques within textual statements and show that a robust methodology comparing a range of ML algorithms is important in the discovery of a performant model.

The purpose of this paper is to reveal the main causes of delays in the projects are from the client (relative importance index (RII)=0.716), labor and equipment (RII=0.701) and contractor (RII=0.698). Hence determining the contractual responsibility of delay is the most likely source of dispute in construction projects and many techniques have been used in the courts to demonstrate the criticalities of a delay event on the project schedule. Therefore, authors try to investigate all process-based techniques of delay claims and evaluated and conformed them with principles by Society of Construction Law (SCL) protocol and Association for the Advancement of Cost Engineering International (AACEI) in order to choose the best techniques based on the specific circumstances of each project.

This section is divided into two distinct parts: refers to the methods used to assess the perceptions of clients, consultants, and contractors on the relative importance of causes of delay in construction industry; and refers to advantages and disadvantages of various techniques used to analyze delays and their conform with SCL protocol. A questionnaire was developed to assess the perceptions of clients, consultants, and contractors on the relative importance of causes of delay in Iranian construction industry. The respondents were asked to indicate their response category on 78 well-recognized construction delay factors identified by authors.

In total, 78 causes of delay were identified through research. The identified causes are combined into seven groups. The field survey included 58 contractors, 55 consultants, and 62 client. Data collected were analyzed by RII and Statistical Package for Social Sciences (SPSS). The authors identified main causes of delay and ten most important causes, according to Table AII, from the perspective of three major groups of participants (clients, consultants and contractors). The ranking of categories of causes of delay, according to Table I, were: client-related causes (RII=0.716); labor and equipment category causes (RII=0.701); contractor-related causes (RII=0.698); material-related causes (RII=0.690); design-related causes (RII=0.666); external causes (RII=0.662); and consultant-related causes (RII=0.662). But according to the discussions and given that determining the contractual responsibility of delay is the most likely source of dispute in construction industry and many techniques have been used in the courts to demonstrate the criticalities of a delay event on the project schedule.

All process-based techniques of delay analysis have been present in this paper and categorized in 11 groups. In order to understand the advantages and disadvantages of them by clients, contractor and consultant, a thorough review conducted to reveal the nature of techniques. In the next step, given that selecting the most appropriate technique based on constraints and specific conditions of each project is one of the most important steps to carry out a successful delay analysis. The authors conformed, all process-based techniques of delay analysis, by SCL protocol and AACEI principles. Finally, the result of this match was brought in order to choose the best technique based on the specific circumstances of each project.

The purpose of this study is to present a review of the literature on strategic management accounting (SMA). Specifically, it focuses on the trend of SMA research since the publication of Langfield-Smith’s (2008) influential paper “Strategic management accounting: how far have we come in 25 years?” which raised the question of relevance of further SMA research.

The study reviewed articles published on SMA as a whole (comprising a set of advanced management accounting techniques) and its specific techniques for the period of 2008 to 2019 in 23 leading accounting journals.

The review finds that research on SMA has focused on the contingencies influencing the adoption and implementation of SMA techniques and the effects of such adoption on various aspects of firm and employee performance. The renovation and modification of existing practices in attempt to match with the organizational context has also attracted the attention of several SMA scholars. In addition, a noticeable shift to the strategic management theory and case study method was observed during the study period.

The study focuses on the trend of SMA research in an attempt to revisit the relevance of further research in this arena, particularly as a response to the criticism raised by Langfield-Smith (2008).

The safety of operators handling pesticides is still one of the main problems facing Thai agricultural workers. The purpose of this paper is to study the safety of farmers and the techniques recommended to farmers by the Department of Agriculture in Thailand, i.e. spraying to achieve optimum spray volume with consideration for wind direction – henceforth referred to as officer techniques.

Operator exposure was detected by verifying the deposition of dye tracer on the coveralls worn by ten spray service team leaders, for all applications between May and June 2017. For each technique, a total of 15 patches were attached at the lower legs, thigh, chest, forearms, upper arms, hands, face, forehead and back. Each individual technique was performed four times in the area of 1,800 m².

The results showed that the deposits with the farmers’ techniques was much higher than with officers’ techniques ranging from 2.32 to 23.91 times at the tillering stage and 9.90 to 56.79 times at the booting stage, respectively. These results indicate that the spray application technique has a considerable potential for reducing the contamination of spray operators by 56.96–98.23 percent. Operator safety can be considerably improved by the spray application technique employed. Without any investment and changing equipment, only by considering wind direction, officers’ techniques could avoid much deposition, which is the most practical operation in the field. The boom sprayer as a novel recommended technique is an alternative giving a positive result and it can be a substitute for the conventional method. Furthermore, the authors must pay attention to personal protective equipment (PPE) because depositions were discovered on the whole of the bodies of those tested. PPE is the best way to protect an operator from pesticide contamination.

Operator exposure data can be helpful in further development of exposure models and databases for risk assessment and pesticide registration in Thailand.

The purpose of this study is to review the empirical studies that have focused on the adoption, benefits and contingencies of strategic management accounting (SMA) practices and the effects of adoption on firm performance.

The study has highlighted empirical studies conducted on SMA practices in the context of both developed [1] and developing economies. In reviewing the literature, the study focuses on the findings of developed economy separately from that of developing economy to get more insight into the differences in the practices of the two set of economies. Based on the review, avenues for future research studies are outlined.

The review of extant literature reveals that several SMA techniques such as competitor accounting, strategic pricing, benchmarking and customer accounting have been highly or moderately adopted in several developed countries while majority of other techniques remained at the bottom line of the adoption status. However, the review demonstrates substantial differences in the SMA practices between the two set of economies in terms of the level of adoption, contingent factors and the effects of adoption.

The study attempts to focus on empirical studies that have concentrated exclusively on SMA practices. The adoption status, benefits derived, contingent factors affecting the adoption decision and the effect of adopting a package of SMA techniques on several aspects of firm performance are presented in the context of both developed and developing economies.

A significant number of studies have been conducted to analyze and understand the relationship between gas emissions and global temperature using conventional statistical approaches. However, these techniques follow assumptions of probabilistic modeling, where results can be associated with large errors. Furthermore, such traditional techniques cannot be applied to imprecise data. The purpose of this paper is to avoid strict assumptions when studying the complex relationships between variables by using the three innovative, up-to-date, statistical modeling tools: adaptive neuro-fuzzy inference systems (ANFIS), artificial neural networks (ANNs) and fuzzy time series models.

These three approaches enabled us to effectively represent the relationship between global carbon dioxide (CO₂) emissions from the energy sector (oil, gas and coal) and the average global temperature increase. Temperature was used in this study (1900-2012). Investigations were conducted into the predictive power and performance of different fuzzy techniques against conventional methods and among the fuzzy techniques themselves.

A performance comparison of the ANFIS model against conventional techniques showed that the root means square error (RMSE) of ANFIS and conventional techniques were found to be 0.1157 and 0.1915, respectively. On the other hand, the correlation coefficients of ANN and the conventional technique were computed to be 0.93 and 0.69, respectively. Furthermore, the fuzzy-based time series analysis of CO₂ emissions and average global temperature using three fuzzy time series modeling techniques (Singh, Abbasov–Mamedova and NFTS) showed that the RMSE of fuzzy and conventional time series models were 110.51 and 1237.10, respectively.

The paper provides more awareness about fuzzy techniques application in CO₂ emissions studies.

These techniques can be extended to other models to assess the impact of CO₂ emission from other sectors.

To demonstrate transparency and accountability, the three boards in this study are required to meet in public in front of an audience, although the boards reserve confidential issues for discussion in private sessions. This study examines boardroom public accountability, contrasting it with accountability in board meetings held in private. The study adopts Erving Goffman's impression management theory to interpret divergences between boardroom behaviour in public and private, or “frontstage” and “backstage” in Goffman's terminology.

The research observes and video-records three board meetings for each of the three boards (nine board meetings), in public and private. The research operationalises accountability in terms of director-manager question-and-answer interactions.

In the presence of an audience of local stakeholders, the boards employ impression management techniques to demonstrate accountability, by creating the impression that non-executive directors are performing challenge and managers are providing satisfactory answers. Thus, they “save the show” in Goffman terms. These techniques enable board members and managers to navigate the interface between demonstrating the required good governance and the competence of the organisations and their managers, while not revealing issues that could tarnish their image and concern the stakeholders. The boards need to demonstrate to the audience that “matters are what they appear to be”, even if they are not. The research identifies behaviour consistent with impression management to manage this complexity. The authors conclude that regulatory objectives have not met their transparency aspirations.

For the first time, the research studies the effect of transparency regulations (“sunshine” laws) on the behaviour of boards of directors meeting in public. The study contributes to the embryonic literature based on video-taped board meetings to access the “black box” of the boardroom, which permits a study of impression management at board meetings not previously possible. This study extends prior impression management theory by identifying eleven impression management techniques that non-executive directors and managers use and which are unique to a boardroom context.

Background: Geostatistics focuses on spatial or spatiotemporal datasets. Geostatistics was initially developed to generate probability distribution predictions of ore grade in the mining industry; however, it has been successfully applied in diverse scientific disciplines. This technique includes univariate, multivariate, and simulations. Kriging geostatistical methods, simple, ordinary, and universal Kriging, are not multivariate models in the usual statistical function. Notwithstanding, simple, ordinary, and universal kriging techniques utilize random function models that include unlimited random variables while modeling one attribute. The coKriging technique is a multivariate estimation method that simultaneously models two or more attributes defined with the same domains as coregionalization.

Objective: This study investigates the impact of populations on traffic volumes as a variable. The additional variable determines the strength or accuracy obtained when data integration is adopted. In addition, this is to help improve the estimation of annual average daily traffic (AADT).

Methods procedures, process: The investigation adopts the coKriging technique with AADT data from 2009 to 2016 from Montana, Minnesota, and Washington as primary attributes and population as a controlling factor (second variable). CK is implemented for this study after reviewing the literature and work completed by comparing it with other geostatistical methods.

Results, observations, and conclusions: The Investigation employed two variables. The data integration methods employed in CK yield more reliable models because their strength is drawn from multiple variables. The cross-validation results of the model types explored with the CK technique successfully evaluate the interpolation technique's performance and help select optimal models for each state. The results from Montana and Minnesota models accurately represent the states' traffic and population density. The Washington model had a few exceptions. However, the secondary attribute helped yield an accurate interpretation. Consequently, the impact of tourism, shopping, recreation centers, and possible transiting patterns throughout the state is worth exploring.

This study aims to examine the impact of implementing target costing and continuous improvement techniques in industrial companies operating in southern Palestine on achieving sustainable competitive advantage (SCA). The study mainly assesses the level of application of these techniques by Palestinian industrial companies (PICs). Furthermore, it evaluates the extent to which the integration of these two methods can impact SCA, by producing cost-effective and innovative products that meet customer demands and needs, while simultaneously achieving continuous development of the company and an SCA.

A descriptive analytical approach was used to study the target costing and continuous improvement techniques employed by industrial companies in southern Palestine. A questionnaire was administered to 415 companies in the southern West Bank to collect data on the application of target cost and continuous improvement and their impact on SCA, measured through market share, differentiation and cost reduction. Control variables, such as company age, size (measured by the number of employees) and industrial sector classification were also included in the study model.

The findings of the study revealed that the PICs apply target costing and continuous improvement at a high level. Furthermore, all dimensions of achieving SCA were found to be achieved at a high level, with market share being the most prominent. The study also found that the integration of the target costing and continuous improvement had a positive impact on achieving SCA in the PICs. However, the study found no impact on company size, age or industrial sector on achieving a competitive advantage in terms of market share or other results.

The current study was limited to the application of strategic management methods to companies within the industrial sector only. This may constitute a limitation because it neglected other sectors. Likely, another limitation was the difficulty of obtaining the quantitative numbers needed for some quantitative variables that pertain to that type of industrial companies, which are mostly family companies that could not be regulated by the local companies' law to disclose their financial statement.

If industrial companies have ambitions to reduce production costs from the planning and design stage to set the target selling price. It is based on the understanding and awareness of customers' desires while maintaining the quality of products according to the best methods of improvement and innovation; therefore, this can be achieved by using the target costing and the continuous improvement techniques through reviewing the current study and its results.

The current study sought to link two methods, simultaneously and complementary, with each other of the strategic methods of managerial accounting, which helps the companies to offer their best to attract customers, develop the product or service and maintain their continuity in a changing labor market that enables it to achieve sustainable and competitive advantage.

This study is unique in that it explores the impact of the integration of target costing or continuous improvement techniques (Kaizen) on achieving SCA in Palestine industrial companies. While previous studies have examined either target costing or continuous improvement techniques separately, this study enhances the integration of these two methods to achieve SCA.

Mouth segmentation is one of the challenging tasks of development in lip reading applications due to illumination, low chromatic contrast and complex mouth appearance. Recently, deep learning methods effectively solved mouth segmentation problems with state-of-the-art performances. This study presents a modified Mobile DeepLabV3 based technique with a comprehensive evaluation based on mouth datasets.

This paper presents a novel approach to mouth segmentation by Mobile DeepLabV3 technique with integrating decode and auxiliary heads. Extensive data augmentation, online hard example mining (OHEM) and transfer learning have been applied. CelebAMask-HQ and the mouth dataset from 15 healthy subjects in the department of rehabilitation medicine, Ramathibodi hospital, are used in validation for mouth segmentation performance.

Extensive data augmentation, OHEM and transfer learning had been performed in this study. This technique achieved better performance on CelebAMask-HQ than existing segmentation techniques with a mean Jaccard similarity coefficient (JSC), mean classification accuracy and mean Dice similarity coefficient (DSC) of 0.8640, 93.34% and 0.9267, respectively. This technique also achieved better performance on the mouth dataset with a mean JSC, mean classification accuracy and mean DSC of 0.8834, 94.87% and 0.9367, respectively. The proposed technique achieved inference time usage per image of 48.12 ms.

The modified Mobile DeepLabV3 technique was developed with extensive data augmentation, OHEM and transfer learning. This technique gained better mouth segmentation performance than existing techniques. This makes it suitable for implementation in further lip-reading applications.