We analyze charity requests registered on the Random Acts of Pizza online community and examine the content of postings and non-content characteristics to identify features that are associated with the success of donation. We find that the presence of rational and credible appeals in a message increases the likelihood of receiving a donation, whereas the mere presence of negative emotional appeal does not do so. Our research is useful for those who like to make persuasive charity requests on online platforms. 

In this research we enquire if adoption of Twitter by manufacturing firms creates any value for the firm. We conduct two studies to examine the relationship between Twitter related activities of manufacturing firms and the market reaction towards these firms. We collect a novel multi-period dataset and analyse the overall impact of adoption of Twitter on Tobin's Q by employing a propensity score matching and difference-in-difference research design. Our findings suggest that adoption of Twitter increases the value of the firm post adoption. We also conduct additional robustness check such as use of Industry Week data as a proxy of firm value and find our results to be consistent. We adopt a text mining-based approach and examine the communication environment of the manufacturing firms. We use the Latent Dirichlet Allocation (LDA) algorithm for short texts and identify six broad topics among tweets posted by firms. Our panel regression based analysis suggests that there is positive association between divulging product related information and Tobin's Q. Our research showcases the strong impact of use of Twitter and contributes to the nascent literature on firm generated content. It is likely to encourage managers of manufacturing firms to start actively using Twitter for sharing product related information on social media. 

The content generation strategy of a sports franchise determines whether the user engagement increases or decreases on social media platforms. Thus, the role of Chief Operating Officer (COO) is profound who generally decides and governs social media policies of the franchises. We show that the cultural differences between local-COO vis-à-vis foreign-COO-governed sports franchises reflect in their content generation strategy and are also associated with user engagement. We use Hofstede's cultural dimensions theory and extract relevant features from the tweets. Overall, the results show that user engagement is more when the content generation strategy is in alignment with fans’ national culture. The first contribution of our work is towards showing the incremental impact of power distance, individualism and collectivism on user engagement. The second contribution of our work is towards feature construction, feature selection and building authorship attribution classifiers to understand the content generation strategy. Prior literature shows that national culture impacts writing of online reviews. We investigate the role of national culture in social media content generation and user engagement and extend the literature. Our study is useful for organizations to understand the role of national culture in content generation and how it is related to user engagement. 

There is limited research showing how strategically generated content can boost Twitter engagement. The problem is acute for sports clubs with large fan bases. We determine the ideal content generation strategy using Hofstede's Cultural Dimensions and Language Expectancy theory. This study examines whether culturally aligned tweets can improve fan engagement. Using tweets from a sports league, we demonstrate that culturally aligned features may be used to build machine learning and deep learning models that predict a tweet's engagement level. According to our research, culture-specific social media content that meet fans' language expectations can increase Twitter engagement. 

It is now much discussed that Artificial Intelligence (AI) as a General-Purpose Technology (GPT) can resolve the efficiency problems of industries, including in pharmaceutical markets where productivity challenges continue in costs and time for new drug discovery. But did the COVID-19 pandemic inadvertently accelerate the pace of AI adoption in pharmaceutical innovation? We answer this question using novel data on pharmaceutical patents. We use two different databases to analyze abstracts of pharmaceutical patents applied in the USA. Topic modeling was used to identify patents with technical artifacts and classify them as treated group AI-adopting patents. An AI dictionary is used to match AI-related keywords in the patent abstracts. Subsequently, using a difference-in-differences research design we observe that both presence and count of AI keywords in pharmaceutical patents have increased with pandemic. An increase in AI is also related to reduced time taken from application to publication of a patent suggesting innovation efficiencies in the industry. Finally, we find that results are driven by firms that have already built AI capability in the past. Our results remain consistent with various robustness checks, and we conclude by discussing managerial and policy implications of our findings. 

Market regulators and stock exchanges around the globe need to ensure that investors trade in a fair and efficient manner. The main motive for market surveillance is to make the market more efficient and free from rouge elements. Identification of financial rumors is vital for orderly functioning of the stock market. Social media platforms allow spread of unverified information to a large mass quickly due to their interconnected nature and large number of participating members. Due to the deluge of data over various media channels including social media, manual scanning of financial rumors is inefficient. This necessitates the use of a big data infrastructure for collection, storage, and analysis of financial news related data. In this paper, we introduce a framework for automated detection of financial rumors using big data. Our framework is based on extant research on knowledge-based discovery in databases and detection of fraudulent financial activities. We describe an in-depth descriptive case study of the world’s fastest stock exchange, the Bombay Stock Exchange. Through the case, we highlight the importance of analytics for detection of financial rumors and the importance of the big data infrastructure to carry out such a task. We identify several critical factors that lead to successful identification of financial rumors. We believe the framework can be used by market regulators, stock exchanges, and security research agencies to identify information-based market manipulation using a systematic data-driven approach over a big data infrastructure. 

Since its inception, the third party logistics (3PL) industry has remained an area of interest for academicians and practitioners. The existing literature mostly focuses on single multi criteria decision making (MCDM) method-based holistic performance evaluations of 3PL service providers, whereas distinct operational and financial performance measurements have not received enough attention. Several real-life examples of organizations, such as Hub Group and DSV, indicate that the reliance on financial performance improvement solely does not ensure better operational performance and integrated performance, and vice versa. Additionally, there is an absence of works that focus on designing an integrated MCDM methodology that applies multiple MCDM methods to increase the robustness of the methodology and consider distinct operational, financial, and integrated performance measurements of the 3PL service providers. Additionally, the application of emerging ratio analysis-based MCDM methods such as multi objective optimization based on ratio analysis (MOORA) and complex proportional assessment (COPRA) for performance evaluation has been ignored. Furthermore, the assessment of the service quality of 3PL service providers through their customers’ feedback and the association of this service quality with the abovementioned performance measures have not received enough attention. This motivates us to design a criteria importance through intercriteria correlation (CRITIC) weighting-based integrated MOORA-COPRA MCDM methodology for the performance evaluation of 3PL service providers. We apply our proposed methodology to evaluate the performance of 21 leading 3PL service providers in North America. Additionally, we incorporate text mining methods such as sentiment analysis and topic modeling to analyze the effect of these service providers’ service quality captured through their customers’ reviews on distinct operational, financial, and integrated performance. The insights obtained from the study indicate that service quality (as captured from the consumer reviews) has a positive association with the operational and financial performance of 3PL service providers. 

Abstract

Purpose

In the context of sharing economy, the superhost program of Airbnb emerges as a phenomenal success story that has transformed the tourism industry and garnered humongous popularity. Proper performance evaluation and classification of the superhosts are crucial to incentivize superhosts to maintain higher service quality. The main objective of this paper is to design an integrated multicriteria decision-making (MCDM) method-based performance evaluation and classification framework for the superhosts of Airbnb and to study the variation in various contextual factors such as price, number of listings and cancelation policy across the superhosts.

Design/methodology/approach

This work considers three weighting techniques, mean, entropy and CRITIC-based methods to determine the weights of factors. For each of the weighting techniques, an integrated TOPSIS-MOORA-based performance evaluation method and classification framework have been developed. The proposed methodology has been applied for the performance evaluation of the superhosts (7,308) of New York City using real data from Airbnb.

Findings

From the perspective of performance evaluation, the importance of devising an integrated methodology instead of adopting a single approach has been highlighted using a nonparametric Wilcoxon signed-rank test. As per the context-specific findings, it has been observed that the price and the number of listings are the highest for the superhosts in the topmost category.

Practical implications

The proposed methodology facilitates the design of a leaderboard to motivate service providers to perform better. Also, it can be applicable in other accommodation-sharing economy platforms and ride-sharing platforms.

Originality/value

This is the first work that proposes a performance evaluation and classification framework for the service providers of the sharing economy in the context of tourism industry.

Since its inception, the third party logistics (3PL) industry has remained an area of interest for academicians and practitioners. The existing literature mostly focuses on single multi criteria decision making (MCDM) method-based holistic performance evaluations of 3PL service providers, whereas distinct operational and financial performance measurements have not received enough attention. Several real-life examples of organizations, such as Hub Group and DSV, indicate that the reliance on financial performance improvement solely does not ensure better operational performance and integrated performance, and vice versa. Additionally, there is an absence of works that focus on designing an integrated MCDM methodology that applies multiple MCDM methods to increase the robustness of the methodology and consider distinct operational, financial, and integrated performance measurements of the 3PL service providers. Additionally, the application of emerging ratio analysis-based MCDM methods such as multi objective optimization based on ratio analysis (MOORA) and complex proportional assessment (COPRA) for performance evaluation has been ignored. Furthermore, the assessment of the service quality of 3PL service providers through their customers’ feedback and the association of this service quality with the abovementioned performance measures have not received enough attention. This motivates us to design a criteria importance through intercriteria correlation (CRITIC) weighting-based integrated MOORA-COPRA MCDM methodology for the performance evaluation of 3PL service providers. We apply our proposed methodology to evaluate the performance of 21 leading 3PL service providers in North America. Additionally, we incorporate text mining methods such as sentiment analysis and topic modeling to analyze the effect of these service providers’ service quality captured through their customers’ reviews on distinct operational, financial, and integrated performance. The insights obtained from the study indicate that service quality (as captured from the consumer reviews) has a positive association with the operational and financial performance of 3PL service providers.