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This paper aims to validates a framework using the Uses and Gratifications Theory (UGT) to study the effect of values, i.e. Functional Value (FV), Social Value (SV), Emotional Value (EV) and Monetary Value (MV), on the Patronage Intention (PI) of Social Network Users (SNU’s) with mediating role of Perceived Usefulness (PU).

A survey method was used to collect responses from 302 SNUs, and the variance-based structural equation method was used to understand the relationships among the constructs.

The results found a significant positive effect of FV and EV on Perceived Usefulness (PU) and MV and PU on Patronage intention (PI) of SNUs. Further, PU partially mediated the relationship of EV with PI.

This study used the UGT to understand the effect of values on the PI of SNUs. This research study contributes to the existing social networks/social media literature.

This study discusses the sources of value co-creation and its effects on businesses using social platforms such as Facebook, Instagram, Twitter and blogs.

A total of 301 survey responses were selected. The selected respondents indicated that they lived in South Korea, had an occupation as a private business or marketer and had used a social platform one or more times a day. The hypotheses were tested using a structural equation model.

The study reveals that the source of sustainable value co-creation between social platforms and businesses positively affects the competitive advantage of maintaining businesses. This advantage reveals an integrated relationship that leads to the successful financial performance of businesses through online word of mouth and customer satisfaction. Moreover, this study finds that the relationship between variables differs by social platform types (unidirectional vs bidirectional service).

The results of this study explain the relationship between value co-production, value-in-use, SCA and long-term performance. However, this study focused on private business and marketing staff working in companies in South Korea. Accordingly, more countries in which social platforms are widely utilized should be taken into account to help generalize the empirical findings.

There is a difference in the relationship between co-creation activity and cost advantage/long-term performance in accordance with the service type of a social platform. The results indicate that a bidirectional service is a more powerful tool for cost advantage and long-term performance.

This study focuses on the role of value co-creation in social platforms to ensure companies’ sustainable competitive advantage and performance. The results of this study will help companies develop online marketing strategies using social platforms.

This study is among the first to integrate the technology acceptance model (TAM) and value-based adoption model (VAM) in the context of augmented reality (AR) shopping. It assesses how consumers' rational (TAM) and emotional (VAM) factors influence their intention to use AR in online shopping via perceived value and consumer engagement.

This study uses a quantitative research approach and employs a standardized survey questionnaire distributed on social media platforms to recruit Gen Z members who are potential buyers or users of AR technology. SmartPLS 4.0 was used to test the responses of 204 respondents.

The results indicate that consumers who perceive a higher value of AR in shopping are inclined to use AR in their future shopping when AR shopping is easy to use, useful, personalized, innovative and provides a highly engaging experience. Interestingly, perceived sacrifice did not influence perceived value. This study confirms that integrating TAM and VAM is instrumental in capturing value, which in turn influences engagement and the intention to use AR in online shopping.

This study further extends the conceptualization of AR perceived value by combining rational components derived from TAM and VAM, thus leading to a sturdy and theoretically grounded framework. In addition, this study contributes to the literature on extended reality, namely AR shopping, and helps brand managers manage highly evolving AR experience for Gen Z.

An appraisal is normally conducted to determine financial viability of property development projects for several purposes. The residual valuation method is normally used to appraise such projects and the purpose of the paper is to examine its financial viability decision rules (FVDRs) used by practitioners.

The qualitative research approach was adopted based on the case study strategy of enquiry where 48 development appraisal reports from 37 Royal Institution of Chartered Surveyors registered firms in London were accessed from the internet and critically reviewed.

Site-specific and area-wide development appraisals for planning purposes dominated the reports. Five FVDRs were identified. A development project is financially viable if: (i) computed residual profit expressed as a percentage return is equal to or greater than a determined market benchmark risk-adjusted return; (ii) computed residual profit expressed as a percentage return is positive; (iii) calculated residual land value is greater than open market land value or benchmark land value; (iv) computed residual land value is positive; and (v) there is a surplus when appraisal cost variables including land costs plus allowance for developer’s profit are deducted from gross development value. In some reports, it was discovered some appraisal cost variables were excluded whilst others were inappropriately treated.

The first and third FVDRs are reasonable whilst the remaining are fraught with problems and using them can make development projects that are financially unviable to be viable. Also, excluding relevant cost variables and treating some inappropriately understate the appraisal cost component resulting in incorrect financial viability outcomes. These can lead to wrong recommendations about financial viability being proffered that negatively affect the practitioners’ clientele. The dominance of development appraisals for planning purposes shows the important role development appraisals continue to play in the English planning system.

To the best of the author’s knowledge, it is the first time FVDRs in development appraisals have been systematically investigated in England with resultant new empirical findings and arguments.

There is a widely held belief that open government data (OGD) have the potential to generate both economic and social value. This study aims to empirically unpack the relationship between OGD, diversification activities and innovation in the pursuit of economic value creation by firms.

Using a matched sample comparison method and difference-in-differences analyses, the authors study the impact of OGD on innovation over time in the USA. The authors considered the open government directive in the end of 2009 in the USA as a policy intervention and collected 10 years of financial data of 79 firms that use OGD and 79 matched control firms in the USA. The authors compare US firms using OGD, with matched control firms, regarding the firms’ level of product diversification as a measure of innovative use of OGD.

The authors provide empirical evidence that OGD policy contributes toward innovation, and hence economic value creation, through product diversification. Firms that leverage OGD show superior product diversification in comparison to the matching control firms. The results suggest that OGD contribute to firms’ innovation and pursuit of economic value, as evidenced by their increased product diversification.

Although the extant literature concerning OGD has underscored the impact of OGD on innovation and economic value generation, there is a lack of empirical evidence in the literature. This study seeks to add to the extant literature by providing empirical evidence that contributes to the understanding of the relationship between OGD, diversification and innovation in the pursuit of economic value creation.

The current approach to valuing customers is based on the notion of discounted profit generated by the customers over the lifetime of the relationship, also known as customer lifetime value (CLV). However, in the financial services industry, the customers who contribute the most to the profitability of a firm are also the riskiest customers. If the riskiness of a customer is not considered, firms will overestimate the true value of that customer. This paper proposes a methodology to adjust CLV for different types of risk factors and creates a comprehensive measure of risk-adjusted lifetime value (RALTV).

Using data from a major credit card company, we develop a measure of risk adjusted lifetime value (RALTV) that accounts for diverse types of customer risks. The model is estimated using Stochastic Frontier Analysis (SFA).

Major findings indicate that rewards cardholders and affinity cardholders tend to score higher within the RALTV framework than non-rewards cardholders and non-affinity cardholders, respectively. Among the four different modes of acquisition, the Internet generates the highest RALTV, followed by direct mail.

This paper not only controls for different types of consumer risks in the financial industry and creates a comprehensive risk-adjusted lifetime value (RALTV) model but also shows empirically the value of using RALTV over CLV for predicting future performance of a set of customers. Further, we investigate the impact of a firm’s acquisition and retention strategies on RALTV. The measure of risk-adjusted lifetime value is invaluable for managers in financial services.

This study investigates whether the disclosure of derivatives is value relevant in emerging markets and evaluates the effects of the 2008/2009 global financial crisis on the value relevance of derivative disclosures.

Panel regression models using sub-samples and a crisis interaction term were applied to a sample of the 200 largest non-financial firms by market capitalization listed on the Johannesburg Stock Exchange (JSE) from 2005 to 2017 to assess the consequences of the financial crisis.

The results suggest that the disclosure of derivatives is value relevant in the hitherto understudied context of emerging markets. The 2008/2009 financial crisis had a significant impact on derivatives use and the value relevance of derivatives disclosure by JSE-listed companies.

Companies should reconsider both how they employ derivatives as part of their risk management practices and how they communicate derivatives use to stakeholders in the financial statements. The findings facilitate a comparative analysis across various market contexts by researchers and assist investors in better decision-making. The findings can influence regulatory practices and can help standard setters to review disclosure requirements.

The benefits of corporate hedging were studied from an emerging market perspective, using an original dataset and approach to investigate the effects of international financial volatility on emerging markets. The authors tested whether companies are valued differently, based on their disclosure of the use of derivatives in the financial statements, and the effect of the financial crisis on the value relevance derivatives disclosures.

Earned value management (EVM)–based models for estimating project actual duration (AD) and cost at completion using various methods are continuously developed to improve the accuracy and actualization of predicted values. This study primarily aimed to examine natural gradient boosting (NGBoost-2020) with the classification and regression trees (CART) base model (base learner). To the best of the authors' knowledge, this concept has never been applied to EVM AD forecasting problem. Consequently, the authors compared this method to the single K-nearest neighbor (KNN) method, the ensemble method of extreme gradient boosting (XGBoost-2016) with the CART base model and the optimal equation of EVM, the earned schedule (ES) equation with the performance factor equal to 1 (ES1). The paper also sought to determine the extent to which the World Bank's two legal factors affect countries and how the two legal causes of delay (related to institutional flaws) influence AD prediction models.

In this paper, data from 30 construction projects of various building types in Iran, Pakistan, India, Turkey, Malaysia and Nigeria (due to the high number of delayed projects and the detrimental effects of these delays in these countries) were used to develop three models. The target variable of the models was a dimensionless output, the ratio of estimated duration to completion (ETC(t)) to planned duration (PD). Furthermore, 426 tracking periods were used to build the three models, with 353 samples and 23 projects in the training set, 73 patterns (17% of the total) and six projects (21% of the total) in the testing set. Furthermore, 17 dimensionless input variables were used, including ten variables based on the main variables and performance indices of EVM and several other variables detailed in the study. The three models were subsequently created using Python and several GitHub-hosted codes.

For the testing set of the optimal model (NGBoost), the better percentage mean (better%) of the prediction error (based on projects with a lower error percentage) of the NGBoost compared to two KNN and ES1 single models, as well as the total mean absolute percentage error (MAPE) and mean lags (MeLa) (indicating model stability) were 100, 83.33, 5.62 and 3.17%, respectively. Notably, the total MAPE and MeLa for the NGBoost model testing set, which had ten EVM-based input variables, were 6.74 and 5.20%, respectively. The ensemble artificial intelligence (AI) models exhibited a much lower MAPE than ES1. Additionally, ES1 was less stable in prediction than NGBoost. The possibility of excessive and unusual MAPE and MeLa values occurred only in the two single models. However, on some data sets, ES1 outperformed AI models. NGBoost also outperformed other models, especially single models for most developing countries, and was more accurate than previously presented optimized models. In addition, sensitivity analysis was conducted on the NGBoost predicted outputs of 30 projects using the SHapley Additive exPlanations (SHAP) method. All variables demonstrated an effect on ETC(t)/PD. The results revealed that the most influential input variables in order of importance were actual time (AT) to PD, regulatory quality (RQ), earned duration (ED) to PD, schedule cost index (SCI), planned complete percentage, rule of law (RL), actual complete percentage (ACP) and ETC(t) of the ES optimal equation to PD. The probabilistic hybrid model was selected based on the outputs predicted by the NGBoost and XGBoost models and the MAPE values from three AI models. The 95% prediction interval of the NGBoost–XGBoost model revealed that 96.10 and 98.60% of the actual output values of the testing and training sets are within this interval, respectively.

Due to the use of projects performed in different countries, it was not possible to distribute the questionnaire to the managers and stakeholders of 30 projects in six developing countries. Due to the low number of EVM-based projects in various references, it was unfeasible to utilize other types of projects. Future prospects include evaluating the accuracy and stability of NGBoost for timely and non-fluctuating projects (mostly in developed countries), considering a greater number of legal/institutional variables as input, using legal/institutional/internal/inflation inputs for complex projects with extremely high uncertainty (such as bridge and road construction) and integrating these inputs and NGBoost with new technologies (such as blockchain, radio frequency identification (RFID) systems, building information modeling (BIM) and Internet of things (IoT)).

The legal/intuitive recommendations made to governments are strict control of prices, adequate supervision, removal of additional rules, removal of unfair regulations, clarification of the future trend of a law change, strict monitoring of property rights, simplification of the processes for obtaining permits and elimination of unnecessary changes particularly in developing countries and at the onset of irregular projects with limited information and numerous uncertainties. Furthermore, the managers and stakeholders of this group of projects were informed of the significance of seven construction variables (institutional/legal external risks, internal factors and inflation) at an early stage, using time series (dynamic) models to predict AD, accurate calculation of progress percentage variables, the effectiveness of building type in non-residential projects, regular updating inflation during implementation, effectiveness of employer type in the early stage of public projects in addition to the late stage of private projects, and allocating reserve duration (buffer) in order to respond to institutional/legal risks.

Ensemble methods were optimized in 70% of references. To the authors' knowledge, NGBoost from the set of ensemble methods was not used to estimate construction project duration and delays. NGBoost is an effective method for considering uncertainties in irregular projects and is often implemented in developing countries. Furthermore, AD estimation models do fail to incorporate RQ and RL from the World Bank's worldwide governance indicators (WGI) as risk-based inputs. In addition, the various WGI, EVM and inflation variables are not combined with substantial degrees of delay institutional risks as inputs. Consequently, due to the existence of critical and complex risks in different countries, it is vital to consider legal and institutional factors. This is especially recommended if an in-depth, accurate and reality-based method like SHAP is used for analysis.

This paper presents a Monotonic Unbounded Schemes Transformer (MUST) approach to bound/monotonize (remove undershoots and overshoots) unbounded spatial differencing schemes automatically, and naturally. Automatically means the approach (1) captures the critical cell Peclet number when an unbounded scheme starts to produce physically unrealistic solution automatically, and (2) removes the undershoots and overshoots as part of the formulation without requiring human interventions. Naturally implies, all the terms in the discretization equation of the unbounded spatial differencing scheme are retained.

The authors do not formulate new higher-order scheme. MUST transforms an unbounded higher-order scheme into a bounded higher-order scheme.

The solutions obtained with MUST are identical to those without MUST when the cell Peclet number is smaller than the critical cell Peclet number. For cell Peclet numbers larger than the critical cell Peclet numbers, MUST sets the nodal values to the limiter value which can be derived for the problem at-hand. The authors propose a way to derive the limiter value. The authors tested MUST on the central differencing scheme, the second-order upwind differencing scheme and the QUICK differencing scheme. In all cases tested, MUST is able to (1) capture the critical cell Peclet numbers; the exact locations when overshoots and undershoots occur, and (2) limit the nodal value to the value of the limiter values. These are achieved by retaining all the discretization terms of the respective differencing schemes naturally and accomplished automatically as part of the discretization process. The authors demonstrated MUST using one-dimensional problems. Results for a two-dimensional convection–diffusion problem are shown in Appendix to show generality of MUST.

The authors present an original approach to convert any unbounded scheme to bounded scheme while retaining all the terms in the original discretization equation.

This study aims to provide a timely review concerning the determinants and economic consequences of fair value reporting in real estate industry, as these topics have been gaining momentum in accounting literature recently.

Diverse editorial sources (e.g. Elsevier, Emerald, Meridian Allenpress, Springer, Sage, Taylor & Francis and Wiley-Blackwell) were consulted to identify relevant studies for this review. Keywords used to collect studies include “fair value” and “IAS 40” or “investment property” and “fair value or “fair value and real estate.” This search yields 33 studies published between 2009 and 2023.

The synthesis of reviewed papers suggests that studies were mainly conducted in the European countries after the mandatory adoption of international financial reporting standards (IFRS) in 2005 and the Australian setting. The first stream of research deals with the choice of fair value approach. Reported empirical findings suggest that corporate size and market-to-book ratio are negatively associated with fair value choice, whereas ownership dispersion increases the likelihood of choosing fair value approach. The empirical evidence concerning the determinants of fair value magnitude suggests the type of appraiser represents a key predictor of the extent of fair value use. The second stream of research examines the impact of fair value reporting in real estate industry. Findings suggest that empirical evidence is still limited with respect to creditors, managers and financial analysts; fair value reporting is generally associated with higher level of value relevance for investors; and the use of Level 3 inputs in fair value estimates for investment properties is associated with high degree of estimation uncertainty for external auditors leading to increased audit risk and fees.

With respect to regulators, this review emphasizes that the beneficial impacts of fair value reporting are linked to institutional characteristics (e.g. legal system, the degree of market development), the reliability concerns regarding fair value estimates and the independence of appraiser. Because real estate industry is generally characterized by the lack of active market, regulators may adopt regulations requiring the independence external appraiser.

This literature review represents a historical record and an introduction for accounting scholars, in emerging economies and other settings, where fair value accounting has gained wide acceptance among the investment community. It also offers guidance for future research avenues.