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The paper aims to understand Finnish architects' attitudes towards the use of timber as a structural material in multi-storey (over two--storeys high) residential construction.

The study was conducted through a literature survey mainly including international peer-reviewed journals and similar research projects. Furthermore, the literature survey informed the generation of the web-based survey questionnaire design to gather information on architects' perceptions, attitudes and interest in the use of wood in multi-storey (over two-storeys high) residential buildings.

The paper's findings are as follows: (1) respondents perceived the most important advantages of wood as a lightweight, local and ecological material; (2) wood construction (compared to concrete) included perceived concerns about it being more costly and needing more complex engineering and (3) respondents had a favourable overall attitude towards the use of wood particularly in low-rise residential construction, whilst their perception of tall housing, including timber ones, was mostly negative.

No studies have evaluated the use of wood in tall residential buildings and architects' perceptions in Finland.

Religious tourism is one of the oldest and fastest-growing segments. This study analyzes religious tourism through the pilgrimage of Muslims to the holy city of Mecca and has the following objectives: (1) establish the motivational dimensions of religious tourism; (2) identify the motivational dimensions that predict the satisfaction of religious tourism and (3) determine the motivational dimensions that predict return, recommend and say positive things about religious tourism applied to the pilgrimage to a sacred city.

The sample consisted of Muslim pilgrims who had visited Mecca. The sample was collected in Bahrain, a country located on the Persian Gulf, where most of its population is Muslim. A total of 380 valid questionnaires were obtained online. For the data analysis, factorial analysis and the multiple regression method enter were performed.

The results show that religious motivations are more important when visiting a sacred city than secular ones. Three motivational dimensions were found: religious, social and cultural and shopping. The three dimensions found have a significant relationship with satisfaction and loyalty. Likewise, it was found that the religious motivational dimension is the factor that most predicts satisfaction and loyalty in the behavior of religious visits to a sacred city.

The main limitation of the present study was the temporality in which the sample was taken because the demand may vary at another time of the year and therefore vary its results.

The authors of the study recommend that holy cities increase the religious motivations of these travelers by periodically researching their needs and organizing services to suit their desired spiritual experience. Also, to improve the social and cultural part, travel agencies and tourist companies to Mecca should promote social and cultural motivation among travelers in an appropriate way by providing service packages that involve visits to cultural and social sites such as museums and cultural centers.

This research will serve as a management guide for public institutions and private companies to develop more efficient planning in religious destinations and sacred cities.

This study is the first to analyze the construct of motivations in the pilgrimage to the city of Mecca, to then establish what the main motivations are that predict satisfaction and loyalty in a religious city. Thus, its results provide important information for tourist destination managers and tourism service providers.

The internal (farmer-controlled) and external (non-farmer-controlled) factors affect crop yield. However, not a single study has identified and analyzed yield predictors in India using effective predictive models. Thus, this study aims to investigate how internal and external predictors impact pearl millet yield and Stover yield.

Descriptive analytics and artificial neural network are used to investigate the impact of predictors on pearl millet yield and Stover yield. From descriptive analytics, 473 valid responses were collected from semi-arid zone, and the predictors were categorized into internal and external factors. Multi-layer perceptron-neural network (MLP-NN) model was used in Statistical Package for the Social Sciences version 25 to model them.

The MLP-NN model reveals that rainfall has the highest normalized importance, followed by irrigation frequency, crop rotation frequency, fertilizers type and temperature. The model has an acceptable goodness of fit because the training and testing methods have average root mean square errors of 0.25 and 0.28, respectively. Also, the model has R² values of 0.863 and 0.704, respectively, for both pearl millet and Stover yield.

To the best of the authors’ knowledge, the current study is first of its kind related to impact of predictors of both internal and external factors on pearl millet yield and Stover yield.

The literature reveals that most studies have estimated crop yield using limited parameters and forecasting approaches. However, this research will examine the impact of various predictors such as internal and external of both yields. The outcomes of the study will help policymakers in developing strategies for stakeholders. The current work will improve pearl millet yield literature.

Studies showed that construction digitalisation could prevent or mitigate accidents rate on sites. Digitalisation applications may prevent or mitigate building project collapse (BPC) but with some encumbrances, especially in developing countries. There is a paucity of research on digital technologies application to prevent or mitigate BPC in Nigeria. Thus, the research aims to explore the perceived barriers that may hinder digital technologies from preventing or mitigating building collapse and recommend measures to improve technology applications during development.

The study is exploratory because of the unexplored approach. The researchers collected data from knowledgeable participants in digitalisation and building collapse in Nigeria. The research employed a phenomenology approach and analysed collected data via a thematic approach. The study achieved saturation at the 29th interviewee.

Findings show that lax construction digitalisation implementation, absence of regulatory framework, lax policy, unsafe fieldworkers' behaviours, absence of basic infrastructure, government attitude, hesitation to implement and high technology budget, especially in developing countries, are threats to curbing building collapse menace via digitalisation. The study identified technologies relevant to preventing or mitigating building collapse. Also, it proffered measures to prevent or mitigate building collapse via improved digital technology applications during development.

This research contributes to the construction digitalisation literature, especially in developing countries, and investigates the perceived barriers that may hinder digital technologies usage in preventing or mitigating building collapse in Nigeria.

This study aims to systematically review various behavioral biases that impact an investor’s decision-making process. The prime objective of this paper is to thematically explore the behavioral bias literature and propose a comprehensive framework that can elucidate a more reasonable explanation of changes in financial markets and investors’ behavior.

Systematic literature review (SLR) methodology is applied to a portfolio of 71 peer-reviewed articles collected from different electronic databases between 2007 and 2021. Content analysis of the extant literature is performed to identify the research themes and existing gaps in the literature.

This research identifies publication trends of the behavioral biases literature and uncovers 24 different biases that impact individual investors’ decision-making. Through thematic analysis, an attribute–consequence–impact framework is proposed that explains different biases leading to individual investors’ irrationality. The study further proposes directions for future research by applying the theory–characteristics–context–methodology framework.

The results of this research will help scholars and practitioners in understanding the existence of various behavioral biases and assist them in identifying potential strategies which can evade the negative effects of these biases. The findings will further help the financial service providers to understand these biases and improve the landscape of financial services.

The essence of the current paper is the application of the SLR method on 24 biases in the area of behavioral finance. To the best of the authors’ knowledge, this study is the first attempt of its kind which provides a methodical and comprehensive compilation of both cognitive and emotional behavioral biases that affect the individual investor’s decision-making.

This study aims to provide a comprehensive bibliometric investigation of the antecedents to financial stability in Islamic banking, a transition economy with a volatile stock market focusing on banks following the Shariah approach.

The data for this analysis was extracted from the Scopus database, which combines a comprehensively crafted abstract and citation database with augmented data and linked scholarly works across various disciplines. It quickly finds relevant research and provides access to reliable data and analytical tools. This study deploys “bibliometrix 3.0,” a biblioshiny R-package for influential structure and the VOS viewer for intellectual structure.

The investigation’s main findings revealed that 1,910 documents were published from 1987 to 2022. Published manuscripts received 39,050 citations, with an average of 10.18 citations per year. However, the instructed empirical research was experienced during 2009 and 2020, while earlier periods (1987–2008) were relatively inactive where banking was considered protective in the presence of BASEL-II capital accords regulations. While the International Journal of Bank Market has been at the top of the list to publish articles related to the area under investigation, the Journal of Banking and Finance is ranked one of the most cited articles. Malaysia has been at the top of the list of countries to research Islamic Sharia compliance principles in the banking industry, and International Islamic University Malaysia has produced enough evidence in this regard. The intellectual structure provided essential foundations for future research, and the bibliometric coupling approach was used.

While most of the banking research has been conducted to determine the banking business efficiency, risk and profitability, little focus is given to financial stability and that too concerning the Islamic banks. Therefore, researchers need to investigate this horizon from an Islamic banking point of view and focus on key issues that discriminate between Islamic and conventional banks in determining their stability level.

Briefly, to the best of the authors’ knowledge, this study would be the first to provide bibliometric information about financial stability keeping in view the sample data from banks with the Shariah approach. Furthermore, the proven analysis demonstrates a novel contribution that financially stable Islamic banks might strengthen the financial industry and overall economy.

The purpose of this study is to examine the behaviour of retail investors while making an investment decision and how it gets affected by the behavioural biases of the investors using a moderated-mediation framework.

A mixed method approach has been used to fulfil the objectives of the study. In the first study, a qualitative analysis of the interviews with 15 retail investors was conducted. As part of the quantitative study, a total of 201 responses from Indian retail investors were collected using systematic sampling and analysed using structural equation modelling and Process Macro.

The results indicate that anchoring bias, availability bias, herding bias, switching cost, sunk cost, regret avoidance and perceived threat have a significant effect on retail investors’ investing intention. The attitude of the investors towards investing decisions mediates the effects of behavioural bias and the status quo on investment intention. The results of the moderated-mediation analysis indicate that mediating effect of attitude varied at the low and high-risk aversion of investors.

The findings of this study will help regulators and retail investors to understand the critical behavioural biases which affect the investors’ investing intention.

The paper contributes to the literature on investors’ behaviour, status quo bias theory (SQB) and behavioural bias. This study uniquely proposes a moderated-mediation framework to understand the effects of biases on retail investors’ investment intention.

Although businesses continue to take up artificial intelligence (AI), concerns remain that companies are not realising the full value of their investments. The study aims to provide insights into how AI creates business value by investigating the mediating role of Business Process Management (BPM) capabilities.

The integrative model of IT Business Value was contextualised, and structural equation modelling was applied to validate the proposed serial multiple mediation model using a sample of 448 organisations based in the EU.

The results validate the proposed serial multiple mediation model according to which AI adoption increases organisational performance through decision-making and business process performance. Process automation, organisational learning and process innovation are significant complementary partial mediators, thereby shedding light on how AI creates business value.

In pursuing a complex nomological framework, multiple perspectives on realising business value from AI investments were incorporated. Several moderators presenting complementary organisational resources (e.g. culture, digital maturity, BPM maturity) could be included to identify behaviour in more complex relationships. The ethical and moral issues surrounding AI and its use could also be examined.

The provided insights can help guide organisations towards the most promising AI activities of process automation with AI-enabled decision-making, organisational learning and process innovation to yield business value.

While previous research assumed a moderated relationship, this study extends the growing literature on AI business value by empirically investigating a comprehensive nomological network that links AI adoption to organisational performance in a BPM setting.

The purpose of this study is to describe latent classes explaining the innovation logic in the Finnish construction companies. Innovativeness is a driver of competitive performance and vital to the long-term success of any organisation and company.

Using finite mixture structural equation modelling (FMSEM), the authors have classified innovation logic into latent classes. The method analyses and recognises classes for companies that have similar logic in innovation activities based on the collected data.

Through FMSEM analysis, the authors have identified three latent classes that explain the innovation logic in the Finnish construction companies – LC1: the internal innovators; LC2: the non-innovation-oriented introverts; and LC3: the innovation-oriented extroverts. These three latent classes clearly capture the perceptions within the industry as well as the different characteristics and variables.

The presented latent classes explain innovation logic but is limited to analysing Finnish companies. Also, the research is quantitative by nature and does not increase the understanding in the same manner as qualitative research might capture on more specific aspects.

This paper presents starting points for construction industry companies to intensify innovation activities. It may also indicate more fundamental changes for the structure of construction industry organisations, especially by enabling innovation friendly culture.

This study describes innovation logic in Finnish construction companies through three models (LC1–LC3) by using quantitative data analysed with the FMSEM method. The fundamental innovation challenges in the Finnish construction companies are clarified via the identified latent classes.