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This study examines whether real earnings management (REM) choices are connected with the ownership structure of politically connected businesses (PCBs). The authors also discuss the moderating role of audit quality (AQ) and family control (FC) on the relationship between PCBs and REM.

The authors' study sample comprises firms registered on the Pakistan Stock Exchange (PSE). The sample examines the financial data of the firms that remained listed for the last eight years, i.e. from 2011 to 2018, excluding nonfinance companies and firms with incomplete data. The authors test the hypothesis using feasible generalized least squares (FGLS) regression methods.

The authors find that PCBs show a high level of involvement in income-decreasing REM compared to nonPCBs due to lower litigation risk in REM. However, the authors' results also show that two monitoring mechanisms, AQ and FC, curb the opportunistic behavior of PCBs and reduce the intensity of REM in PCBs.

The findings of the study are beneficial in decision-making for both internal and external stakeholders, such as creditors, shareholders and competitors. In countries like Pakistan, which fall in the category of emerging economies, PCBs show involvement in income-decreasing REM to change the accurate picture of financial information to attain personal goals, and investors in such countries have a low level of knowledge about earnings management strategies; thus, this study offers detailed knowledge and information to investors and shareholders about political connections and REM. This plays a crucial role for regulators in stiffening the rules and regulations to further assist in more secure financial reporting.

This study contributes to the literature by providing a nuanced understanding of the interplay between political connections, REM, FC and AQ in the business context. Second, family-controlled businesses often exhibit distinct characteristics and governance structures compared to nonfamily-controlled firms. Exploring the moderating role of FC in the following relationship could provide valuable insights into how family dynamics influence the financial reporting practices of PCBs. Third, AQ is a critical factor in ensuring financial reporting transparency. However, the interaction between AQ, political connections, and REM remains relatively unexplored. This study explains how audit oversight affects the earnings management behavior of PCBs.

This research studies the development of the evolving dynamic system model and explores the important elements or factors and what detailed attributes are the main influences model in achieving the success of a business, industry and management. It also identifies the real and major differences between static and dynamic business management models and the detailed factors that influence them. Later, this research investigates the benefits/advantages and limitations/disadvantages of some research studies. The studies conducted in this research put more emphasis on the capabilities of system dynamics (SD) in modeling and the ability to measure, analyse and capture problems in business, industry, manufacturing etc.

The research presented in this work is a qualitative research based on a literature review. Publicly available research publications and reports have been used to create a research foundation, identify the research gaps and develop new analyses from the comparative studies. As the literature review progressed, the scope of the literature search was further narrowed down to the development of SD models. Often, references to certain selected literature have been examined to find other relevant literature. To do so, a supporting tool (that connects related articles) provided by Google Scholar, Scopus, and particular journals has been used.

The dynamic business and management model is very different from the static business model in complexity, formality, flexibility, capturing, relationships, advantages, innovation model, new goals, updated information, perspective and problem-solving abilities. The initial approach of a static system was applied in the canvas business model, but further developments can be continued with a dynamic system approach.

Based on this study, which shows that businesses are developing more towards digitalisation, wanting the ability to keep up with the era that is moving so fast and the desire to increase profits, an instrument is needed that can help describe the difficulties of the needs and developments of the future world. This instrument, or tool of SD, is also expected to assist in drawing future models and in building a business with complex variables that can be predicted from the beginning.

This study will contribute to the SD study for many business incubator research studies. Many practical in business incubator management to have a benefit how to achieve the business performance management (BPM) in SD review.

The significant differences between static and dynamics to be used for business research and strategic performance management. This comparative study analyses some SD models from many authors worldwide. Their goals behind their strategic business models and encounter for their respective progress.

GraphQL is a new Open API specification that allows clients to send queries and obtain data flexibly according to their needs. However, a high-complexity GraphQL query may lead to an excessive data volume of the query result, which causes problems such as resource overload of the API server. Therefore, this paper aims to address this issue by predicting the response data volume of a GraphQL query statement.

This paper proposes a GraphQL response data volume prediction approach based on Code2Vec and AutoML. First, a GraphQL query statement is transformed into a path collection of an abstract syntax tree based on the idea of Code2Vec, and then the query is aggregated into a vector with the fixed length. Finally, the response result data volume is predicted by a fully connected neural network. To further improve the prediction accuracy, the prediction results of embedded features are combined with the field features and summary features of the query statement to predict the final response data volume by the AutoML model.

Experiments on two public GraphQL API data sets, GitHub and Yelp, show that the accuracy of the proposed approach is 15.85% and 50.31% higher than existing GraphQL response volume prediction approaches based on machine learning techniques, respectively.

This paper proposes an approach that combines Code2Vec and AutoML for GraphQL query response data volume prediction with higher accuracy.

This study aims at unpacking the multiplicity of the sitting activity in public spaces through the lens of actor-network theory. In line with previous urban research focussing on outdoor activities, such empirical investigation aims to show the importance of the physical aspects of spaces, including seating, in supporting sitting activities as a way of encouraging the use of public space.

This study adopts the overlap between actor–network theory and affordances. It utilises ethnographic research involving frequent users in Dahiyat Al Hussein Park in Amman-Jordan. Data were gathered on the different seat–user relations and the translated sitting activity networks.

Analysis demonstrates different cases of alignment, misalignment and realignment between what is intended and experienced, and where these relations are maintained, disrupted or changed. These findings reveal the multiplicity of sitting activities; this is significant for understanding how they are maintained.

The research suggests a new way of conceptualising the relationship between the physical environment and users and an approach for examining sitting activities. Some studies have applied actor–network theory and/or the concept of “affordance” by highlighting relations between the object and its user and how they create sitting activities. However, only few studies have problematised the multiplicity of sitting when considering seating uses.

Movies critics believe that the diversity of Iranian cinematic genres has decreased over time. The paper aims to answer the following questions: What is the impact of the continuous cooperation between the key nodes on the audience's taste, uniformity of the cinematic genres and the box office? Is there any relationship between the importance of actors in the actors' network and their popularity?

In the artistic world, artists' relationships lead to a network that affects individuals' commercial or artistic success and defines the artwork's value. To study the issue that the diversity of Iranian cinematic genres has decreased over time, the authors utilized social network analysis (SNA), in which every actor is considered a node, and its collaboration with others in the same movies is depicted via edges. After preparing the desired dataset, networks were generated, and metrics were calculated. First, the authors compared the structure of the network with the box office. The results illustrated that the network density growth negatively affects box office. Second, network key nodes were identified, their relationships with other actors were inspected using the Apriori algorithm to examine the density cause and the cinematic genre of key nodes, and their followers were investigated. Finally, the relationship between the actors' Instagram follower count and their importance in the network structure was analyzed to answer whether the generated network is acceptable in society.

The social problem genre has stabilized due to continuous cooperation between the core nodes because network density negatively impacts the box office. As well as, the generated network in the cinema is acceptable by the audience because there is a positive correlation between the importance of actors in the network and their popularity.

The novelty of this paper is investigating the issue raised in the cinema industry and trying to inspect its aspects by utilizing the SNA to deepen the cinematic research and fill the gaps. This study demonstrates a positive correlation between the actors' Instagram follower count and their importance in the network structure, indicating that people follow those central in the actors' network. As well as investigating the network key nodes with a heuristic algorithm using coreness centrality and analyzing their relationships with others through the Apriori algorithm. The authors situated the analysis using a novel and original dataset from the Iranian actors who participated in the Fajr Film Festival from 1998 to 2020.

Introduction: Today the ability of nations to develop and implement innovations is core for their international competitiveness. Ukraine is striving for innovation progress; however, its innovation performance is relatively low. The research problem is to find the bottlenecks, affecting Ukraine’s innovation capability.

Purpose: This study aims to research the national innovation capability profiles, based on cluster analysis, to develop an understanding of drivers and threats for the innovation capability of Ukraine.

Need of the study: The knowledge-based economy, which had already turned into one of the most efficient developmental models of the 21st century, became a key driver of international competitiveness for the leading developed countries due to their progressive structural shifts towards the growth of high-technology manufacturing and knowledge-intensive sectors. These trends are significant to capture for the sake of increasing the innovation capability of the economy of Ukraine.

Methodology: The study is based on the K-means clustering method, which is employed for identifying 10 country clusters based on the indicators of their R&D and innovation activities, which allowed us to assess the innovation capability of Ukraine in comparison with 140 countries of the world. Data selection and normalisation were based on the 2019 Global Competitiveness Report indicators.

Findings: The study showed that Ukraine’s innovation capability problems are typical for most developing countries and are prevalently connected to low R&D expenditures, patent applications, and international co-invention activities. Most countries, except for the technologically developed ones, follow the so-called ‘passive technological learning’ strategies, which usually result in low economic productivity.

Practical implications: Several innovation policy implications have been developed for the government of Ukraine based on the cluster analysis results and accounting for the problems of the national innovation system (NIS).

This study investigates how perceived political corruption, a generally overlooked corruption type, relates to firms' new product development (NPD) through perceived regulatory obstacles. It also examines firms' perceptions of business association support in this relationship, considering these associations' potential support for NPD.

This study conducted an empirical analysis of 1,663 firms in Turkey, a country noted for a history of legislative corruption, and in which there are strong business associations. Drawing the data from the World Bank's 2019 Enterprise Surveys Dataset, this study tested the hypotheses via the two-stage factor score regression method.

This study finds that perceived political corruption significantly relates to NPD negatively through perceived regulatory obstacles. It also finds that the perceived support of business associations to NPD is significantly greater when firms perceive regulatory obstacles but only slight political corruption.

As far as political corruption is concerned, this study reveals that corruption can also be the cause of regulatory obstacles, expanding the common view of corruption as a means of overcoming regulatory obstacles to NPD. In addition, it introduces the role of business associations in this relationship by revealing their support to NPD for different levels of perceived political corruption and regulatory obstacles.

The purpose of this study is to examine the association between the combined roles of chief executive officer (CEO)-chairman titles (CEO duality) and investment efficiency, defined as a lower deviation from expected investment for targeted S-curve firms used to propel an innovation-driven economy. This study also aims to investigate the moderating effect of financial reporting quality on this association.

This paper focuses on the ten targeted S-curve industries – under the definition of the Thailand 4.0 model – listed on the Stock Exchange of Thailand (SET) from 2000 to 2019. Data related to CEO/chairman titles and investment supports were manually collected from the annual reports, the SET market analysis and reporting tool database and the company websites. Financial data used to estimate investment behaviors and discretionary accruals were extracted from 1999. The study analyzes unbalanced panel data using fixed-effects regressions. Additional tests embrace replacing the sample with nontargeted firms, partitioning into granted and nongranted firms, adding CEOs’ demographic moderators, using alternative variable measures and analyzing for lagged independent variables.

The main findings show that CEO duality reduces overinvestment but worsens underinvestment in targeted firms. Financial reporting quality (FRQ) appears to strengthen CEO duality in mitigating extreme spending but has no impact on the association between CEO duality and underinvestment. Additional results, for example, conclude that CEO duality has no association with both over- and underinvesting at nontargeted firms, but its effect becomes positively significant on overinvestment when financial reporting quality is high. The negative association between CEO duality and overinvestment is found only in government-granted and targeted firms. FRQ encourages CEO duality in lowering overinvestment among targeted firms without grants. CEOs’ female and serviced early years appear to elevate those main findings.

These findings assist innovative corporations in choosing a proper leadership structure to cope with investment inefficiency. The research gives the government and regulatory bodies an insight into the qualifications of the leadership structure and financial information that helps them put forward effective policies.

To the best of the author’s knowledge, this study is among the first to establish the association between CEO duality and investment efficiency for innovation-driven firms in a transforming economy. The study fills the gap in the literature on management, accounting and finance by unveiling the interplay between dual leadership and financial reporting in affecting the efficiency of investments.

The purpose is to describe new business opportunities within the Swedish railway industry and to support the development of business models that corresponds with the needs and requirements of Industry 4.0, here denoted as Service Management 4.0.

The study is an in-depth and descriptive case study of the Swedish railway system with specific focus on a railway vehicle maintainer. Public reports, statistics, internal documents, interviews and dialogues forms the basis for the empirical findings.

The article describes the complex business environment of the deregulated Swedish railway industry. Main findings are in the form of identified business opportunities and new business model propositions for one of the key actors, a vehicle maintainer.

The article provides valuable understanding of business strategy development within complex business environments and how maintenance related business models could be developed for reaching Service Management 4.0.

High-resolution remote sensing images possess a wealth of semantic information. However, these images often contain objects of different sizes and distributions, which make the semantic segmentation task challenging. In this paper, a bidirectional feature fusion network (BFFNet) is designed to address this challenge, which aims at increasing the accurate recognition of surface objects in order to effectively classify special features.

There are two main crucial elements in BFFNet. Firstly, the mean-weighted module (MWM) is used to obtain the key features in the main network. Secondly, the proposed polarization enhanced branch network performs feature extraction simultaneously with the main network to obtain different feature information. The authors then fuse these two features in both directions while applying a cross-entropy loss function to monitor the network training process. Finally, BFFNet is validated on two publicly available datasets, Potsdam and Vaihingen.

In this paper, a quantitative analysis method is used to illustrate that the proposed network achieves superior performance of 2–6%, respectively, compared to other mainstream segmentation networks from experimental results on two datasets. Complete ablation experiments are also conducted to demonstrate the effectiveness of the elements in the network. In summary, BFFNet has proven to be effective in achieving accurate identification of small objects and in reducing the effect of shadows on the segmentation process.

The originality of the paper is the proposal of a BFFNet based on multi-scale and multi-attention strategies to improve the ability to accurately segment high-resolution and complex remote sensing images, especially for small objects and shadow-obscured objects.