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Social media (SM) networks offer a golden opportunity for firms that particularly engage in international activities to set up sustainable customer relationships and improve competitiveness. The purpose of this study is to examine the influence of SM adoption on the export intensity (EI) of firms listed on Borsa Istanbul (BIST) for the years 2010–2020. The authors use social media index (SMI) to measure SM adoption and firm size (FSize) as a moderator on exploring the interaction of SM and EI.

Using a sample of 150 firms listed on the BIST Industrials Index, this study explores how the adoption of SM affects EI by using panel data analysis over the period of 2010–2020.

The results indicate that the SMI has a positive and significant effect on the EI. FSize positively moderates the interaction of SMI and EI, indicating that large firms benefit more from the SM in increasing export performance. The findings reflect high potential of EI improvement through adopting right SM policies in emerging markets.

The sample covers only public companies listed on the BIST Industrials Index. Future studies may extend the coverage and include multiple emerging markets to draw generalized results for the export-oriented firms. This research also analyzes solely four SM networks, i.e. Facebook, Instagram, Twitter and YouTube. However, there are many other SM networks that firms use in online marketing in foreign markets. Finally, this research did not discuss the potential factors that could influence the use of SM in emerging market firms.

This study denotes the significant role of SM adoption on the EI of firms in an emerging market setting from the perspective of resource-based view. It presents an insightful approach in understanding the mission played by SM networks in enhancing the EI of Turkish firms. Policymakers may use the findings to develop public support programs to promote the adoption and implementation of the SM among exporting firms in emerging markets.

The study provides evidence on the effects of SM adoption on the EI from the perspective of emerging countries. It also helps to gain a deeper understanding of how different SM platforms contribute to the internationalization of firms.

Building on resource dependence theory and contingency theory (CT) and focusing on an emerging market setting, this study investigates how demographic board diversity (BD) influences the export intensity (EI) of firms listed on Borsa Istanbul (BIST), with the moderating effect of firm size, as a contingency factor, on this interaction.

Using a sample of 65 exporting firms listed on the BIST Industrials Index, this study explores how demographic attributes of board members, represented by the board diversity index (BDI), affects EI by employing panel data analysis over the period of 2016–2020.

The results suggest that there is a negative relationship between BD and EI, but firm size has a positive moderating effect on the association of BD and EI, indicating that large firms with diverse boards are more prone to access foreign markets and make export. The findings further indicate that board size and CEO duality have a negative and significant effect on EI, while marketing intensity has a positive and significant impact.

The sample covers only public companies listed on the BIST Industrials Index, and the impact of board characteristics on the EI is analyzed for a limited time frame, i.e. from 2016 to 2020.

The findings help business executives better understand the contribution of the firm size on the interaction of BD and EI and offers valuable insights to companies to gain a competitive edge in international markets.

The study provides evidence on the effects of board attributes on the EI from the perspective of emerging countries. It also helps to gain a deeper understanding of how board dynamics contribute to the internationalization of companies.

This study aims to investigate the effects of board characteristics on the cost of debt for non-financial companies in the Turkish capital markets.

Using a sample of 211 non-financial companies listed on Borsa Istanbul, this study examines how chairperson gender and board characteristics affect the cost of debt by using panel data analysis over the period of 2016–2020. A system generalized method of moments model is also applied to test the endogeneity issue.

The findings show that the presence of female chairperson and female directors on board reduces the cost of debt and the perceptions of default risk by fund providers, while board independence and board size do not have a significant impact on the cost of debt. The results provide insightful information for companies and policymakers. Companies can alter board composition through gender diversity, while policymakers can introduce new policies in encouraging the presence of female directors on boards.

This study primarily enriches the literature on the effect of board diversity on debt financing cost in a leading emerging market, enabling companies in emerging markets to better mitigate agency costs and finance their investment through effective board composition. Second, it provides evidence that financial institutions consider companies with chairwomen and women directors on the boards less risky and charge them less for debt financing than they do for companies with man chairperson. Finally, the results support policymakers to take actions to increase female presence on board.

The purpose of this study is to investigate the effects of ownership structure, board attributes and eXtensible Business Reporting Language (XBRL) on annual financial reporting timeliness of non-financial companies listed on Borsa Istanbul (BIST).

To conduct the analyses, the authors used two samples. The main sample consists of 187 companies, while the subsample includes 54 companies in the BIST 100 index. The data set covers the 2010–2018 period. To investigate the influence of ownership structure, board attributes and XBRL on timeliness, panel regression and univariate analyses were used. To explore the factors associated with the likelihood of late filing, panel logistic regression analyses were employed.

The findings provide evidence that companies that have a high level of institutional ownership and women board membership file earlier. In line with prior studies, profitable companies file their accounts faster. Highly leveraged companies are late reporters. Further, XBRL has a positive influence on the filing of financial reports for the BIST 100 companies due to technological agility. Finally, companies that have less institutional ownership and that get qualified audit opinions are more subject to late filing.

The authors acknowledge that this study has certain limitations. First, the results may not be generalized to the entire BIST population due to the exclusion of financial companies from the samples. Future research may explore the financial reporting timeliness of these companies. Second, the study did not investigate the relationship between timeliness and the information content in financial statements and the market reactions they arouse. Third, this study is trying to find out early evidence on the mandatory adoption of XBRL filings, which cover only three-year period due to the recent implementation of this regulatory practice. Thus, it needs further elaboration after the accumulation of data in the forthcoming years by the expansion of the sample beyond the 2016–2018 period. As companies would have more time to become familiar with XBRL, a more reliable conclusion may be drawn. Further, the study particularly focuses on the effect of XBRL adoption on the timeliness among filers. XBRL could also influence investors, auditors and other stakeholders. Future research could investigate the influence of XBRL on different stakeholders to produce more insightful implications.

This study offers several implications for managers, regulators and policy makers. First, companies that do not make timely financial reporting may find it more difficult to attract long-term capital by means of institutional investors. Since these investors view timely reporting as an ideal ingredient in corporate governance, it may have a positive impact on company reputation and corporate sustainability. The results also provide insights for regulatory authorities, policy makers and auditors on the causes of the reporting lag, thereby increasing their awareness and helping them in their decision-making process since improvements in timely availability and accessibility of financial information reduce information asymmetry for users and increase market efficiency. Additionally, companies that reduce their filing timeframe will be able to compare their results with other companies. However, the XBRL mandate could be much more burdensome to smaller firms. This may stem from the fact that larger firms may tend to use the in-house approach for XBRL and can afford more advanced financial reporting systems with automated coding algorithms attached to streamline their XBRL filings, whereas smaller firms are more likely to use the outsourcing approach due to the difference in the level of resources available for XBRL preparation. This finding also lends support to recent concerns that new technology creates an unleveled benefit in reporting efficiency for large companies, but not for small ones (e.g. Blankespoor et al., 2014). This benefit may change the dynamics of the financial market and information environment, leading to further segmentation of the capital markets. The positive effects of XBRL adoption may accrue over time due to the potential benefits of learning curve experience since the XBRL mandate will help companies automate their reporting process and information processing, thereby strengthening internal control over financial reporting (Deloitte, 2013; Du et al., 2013; Li, 2017). Companies may also efficiently incorporate auditor-proposed adjustments by cross-referencing impacted accounts and prepare revised versions of the financial reports, which are automatically rendered in various formats for auditors to assess (Wu and Vasarhelyi, 2004). Finally, investors and other users of financial information benefit from having quicker access to data, since this allows them to make more timely and reliable decisions, leading to greater benefits.

This paper contributes to the literature on the impact of adopting XBRL on the timeliness of financial reporting in emerging markets. Second, this study extends the literature and provides evidence on determinants of timeliness, covering both ownership structure and board attributes besides firm-specific characteristics. Hence, it provides valuable insights for companies, investors, auditing firms and policy makers.

The purpose of this study is to investigate the impact of board demographic diversity on the dividend payout policy in Turkish capital markets.

Using a sample of 67 non-financial companies listed on Borsa Istanbul 100 index from 2013 to 2018, this study examines the influence of board demographic diversity on dividend payout policies in Turkish capital markets. The authors also create a Demographic Board Diversity Index (DBDI) to estimate the composite cognitive diversity. The authors use dividend payment probability, dividend payout ratio, and dividend yield to measure the dividend policy and employ panel logit and tobit regression models.

The results indicate that diversity in nationality, experience and educational background play an influential role in encouraging companies to pay high dividends, while gender, tenure and age diversity are insignificant in affecting dividend payments. The findings also suggest that the DBDI positively affects the companies in formulating the dividend payout policies. Finally, the findings show that the family-owned companies with diverse board members have a negative influence on dividend payment intensity.

The results offer valuable insights for companies and policymakers in emerging markets to develop a more refined governance structure accommodating board demographic diversity attributes to mitigate agency conflicts between controlling and minority shareholders through setting up effective dividend payout policies.

The most costly part in an open-pit mine is the transportation of material out of the mine. The efficiency of the truck-and-shovel fleet plays a major role in cost control. The paper aims to discuss these issues.

The truck dispatching simulation model with consideration of age-based maintenance is proposed.

This paper underlines an impact of truck dispatching decisions and reveals remarkable differences in the total production under different approaches of operational availability. Additionally, the simulated results introduce an effective scheduled maintenance for different truck age levels.

The approach is based on a case study taking into account the stochastic equipment behavior and environment in a real open-pit mine. This approach can be used more generally in situations in which truck fleets are used to transport material.

This paper aims to use text mining methods to explore the similarities and differences between countries’ national digital transformation (DT) and Industry 4.0 (I4.0) policies. The study examines the applicability of text mining as an alternative for comprehensive clustering of national I4.0 and DT strategies, encouraging policy researchers toward data science that can offer rapid policy analysis and benchmarking.

With an exploratory research approach, topic modeling, principal component analysis and unsupervised machine learning algorithms (k-means and hierarchical clustering) are used for clustering national I4.0 and DT strategies. This paper uses a corpus of policy documents and related scientific publications from several countries and integrate their science and technology performance. The paper also presents the positioning of Türkiye’s I4.0 and DT national policy as a case from a developing country context.

Text mining provides meaningful clustering results on similarities and differences between countries regarding their national I4.0 and DT policies, aligned with their geographic, economic and political circumstances. Findings also shed light on the DT strategic landscape and the key themes spanning various policy dimensions. Drawing from the Turkish case, political options are discussed in the context of developing (follower) countries’ I4.0 and DT.

The paper reveals meaningful clustering results on similarities and differences between countries regarding their national I4.0 and DT policies, reflecting political proximities aligned with their geographic, economic and political circumstances. This can help policymakers to comparatively understand national DT and I4.0 policies and use this knowledge to reflect collaborative and competitive measures to their policies.

This paper provides a unique combined methodology for text mining-based policy analysis in the DT context, which has not been adopted. In an era where computational social science and machine learning have gained importance and adaptability to political and social science fields, and in the technology and innovation management discipline, clustering applications showed similar and different policy patterns in a timely and unbiased manner.

When a large number of project proposals are evaluated to allocate available funds, grouping them based on their similarities is beneficial. Current approaches to group proposals are primarily based on manual matching of similar topics, discipline areas and keywords declared by project applicants. When the number of proposals increases, this task becomes complex and requires excessive time. This paper aims to demonstrate how to effectively use the rich information in the titles and abstracts of Turkish project proposals to group them automatically.

This study proposes a model that effectively groups Turkish project proposals by combining word embedding, clustering and classification techniques. The proposed model uses FastText, BERT and term frequency/inverse document frequency (TF/IDF) word-embedding techniques to extract terms from the titles and abstracts of project proposals in Turkish. The extracted terms were grouped using both the clustering and classification techniques. Natural groups contained within the corpus were discovered using k-means, k-means++, k-medoids and agglomerative clustering algorithms. Additionally, this study employs classification approaches to predict the target class for each document in the corpus. To classify project proposals, various classifiers, including k-nearest neighbors (KNN), support vector machines (SVM), artificial neural networks (ANN), classification and regression trees (CART) and random forest (RF), are used. Empirical experiments were conducted to validate the effectiveness of the proposed method by using real data from the Istanbul Development Agency.

The results show that the generated word embeddings can effectively represent proposal texts as vectors, and can be used as inputs for clustering or classification algorithms. Using clustering algorithms, the document corpus is divided into five groups. In addition, the results demonstrate that the proposals can easily be categorized into predefined categories using classification algorithms. SVM-Linear achieved the highest prediction accuracy (89.2%) with the FastText word embedding method. A comparison of manual grouping with automatic classification and clustering results revealed that both classification and clustering techniques have a high success rate.

The proposed model automatically benefits from the rich information in project proposals and significantly reduces numerous time-consuming tasks that managers must perform manually. Thus, it eliminates the drawbacks of the current manual methods and yields significantly more accurate results. In the future, additional experiments should be conducted to validate the proposed method using data from other funding organizations.

This study presents the application of word embedding methods to effectively use the rich information in the titles and abstracts of Turkish project proposals. Existing research studies focus on the automatic grouping of proposals; traditional frequency-based word embedding methods are used for feature extraction methods to represent project proposals. Unlike previous research, this study employs two outperforming neural network-based textual feature extraction techniques to obtain terms representing the proposals: BERT as a contextual word embedding method and FastText as a static word embedding method. Moreover, to the best of our knowledge, there has been no research conducted on the grouping of project proposals in Turkish.

Responding to an increasing call for a more comprehensive conceptualization of customer delight, the purpose of this paper is to expand the theory of customer delight and to examine the implications of such an expanded view for service theory and practice.

This paper presents the results of three qualitative studies. The first study explores customer delight through self-reported consumption experiences in customer-selected contexts, followed by one-on-one in-depth interviews. The second involves focus groups and the third examines self-reported incidents of delightful customer experiences.

This research finds that customer delight goes beyond extreme satisfaction and joy and surprise to include six properties that—individually or in combination—characterize customer delight. An expanded conceptualization of how customer delight can be defined is proposed in which customer delight is associated with various combinations of six properties – the customer experiencing positive emotions, interacting with others, successful problem-solving, engaging customer’s senses, timing of the events and sense of control that characterizes the customer's encounter.

It is clear from the findings of this research that there is no single property that is associated with delight. Through the facilitation of multiple properties, managers have the potential to create a multitude of routes to delight. It is recommended that future research (1) identify and explicate these alternative routes for engendering delight using the six properties identified, and (2) develop a general typology based on service context and characteristics, customer segment, etc. that further stimulates scholarship on delight, and offers more industry-specific insights for managers.

Insights from this investigation will encourage managers and service designers to think more broadly and creatively about delight. Doing so will open up new opportunities for achieving customer delight, beyond merely focusing on extreme satisfaction or surprise and joy strategies currently dominating discussions of customer delight.

This paper makes several contributions to the service literature. First, it extends current conceptualizations of customer delight and offers an expanded definition. Next, it demonstrates how this new understanding extends the existing literature on delight. Finally, it proposes an agenda for future delight research and discusses managerial implications, opening up new opportunities for firms to design delightful customer experiences.