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This case uses an array of carefully selected and excerpted revenue recognition related information contained in Salesforce.com's January 31, 2019, 10-K. Maria, the fictional protagonist, is seeking to understand those disclosures as part of her preparation for an upcoming job interview with the company. As such, she is relying on those disclosures to provide insights as to the company's main product/service lines, the events that signal when and how much revenue the company has earned (i.e., the essence of its business model), along with the related official generally accepted accounting principles (GAAP) criteria pertinent to the valuing and timing of recorded revenues.

This study is the first to investigate the causal relationship between Bitcoin and equity price returns by sectors. Previous studies have focused on aggregated indices such as S&P500, Nasdaq and Dow Jones, but this study uses mixed frequency and disaggregated data at the sectoral level. This allows the authors to examine the nature, direction and strength of causality between Bitcoin and equity prices in different sectors in more detail.

This paper utilizes an Unrestricted Asymmetric Mixed Data Sampling (U-AMIDAS) model to investigate the effect of high-frequency Bitcoin returns on a low-frequency series equity returns. This study also examines causality running from equity to Bitcoin returns by sector. The sample period covers United States (US) data from 3 Jan 2011 to 14 April 2023 across nine sectors: materials, energy, financial, industrial, technology, consumer staples, utilities, health and consumer discretionary.

The study found that there is no causality running from Bitcoin to equity returns in any sector except for the technology sector. In the tech sector, lagged Bitcoin returns Granger cause changes in future equity prices asymmetrically. This means that falling Bitcoin prices significantly influence the tech sector during market pullbacks, but the opposite cannot be said during market rallies. The findings are consistent with those of other studies that have established that during market pullbacks, individual asset prices have a tendency to decline together, whereas during market rallies, they have a tendency to rise independently. In contrast, this study finds evidence of causality running from all sectors of the equity market to Bitcoin.

The findings have significant implications for investors and fund managers, emphasizing the need to consider the asymmetric causality between Bitcoin and the tech sector. Investors should avoid excessive exposure to both Bitcoin and tech stocks in their portfolio, as this may lead to significant drawdowns during market corrections. Diversification across different asset classes and sectors may be a more prudent strategy to mitigate such risks.

The study's findings underscore the need for investors to pay close attention to the frequency and disaggregation of data by sector in order to fully understand the true extent of the relationship between Bitcoin and the equity market.

Why is it that, despite repeated claims that digital-content firms and internet-based businesses can internationalize everywhere almost instantly, many seem unable to profitably expand outside their home markets? Why have emerging market firms (EMNEs) caught up with established developed-country multinationals (DMNEs) so much faster than expected? In this chapter, the author argues that the clue to these two puzzles lies in the realization that, contrary to the dominant view in the international business (IB) literature that focuses only on the intangibles exploited by DMNEs and assumes that these firms are free to unilaterally decide on their mode of entry and operation, doing business in a foreign country is only possible if intangibles are bundled with complementary local resources, usually held by local firms. Taking into account these complementary local resources and their owners makes it clear that DMNEs are not always free to choose their entry mode but must enlist the cooperation of local resource owners. The need of digital-content and internet-based firms for local complementary resources also explains why they sometimes experience problems when expanding abroad. Lastly, control of complementary local resources provides EMNEs with a home advantage against DMNEs competing with them in their home market. The author shows how EMNEs can capitalize on this advantage to obtain the intangibles they lack and need. The fact that these advantages are available on efficient global markets, while complementary local resources are not, explains the surprising speed of EMNE catch-up.

This short case challenges students to review an array of corporate financial metrics and to match them to one of 13 listed industries. As such, students must use their intuition and common sense pertaining to the distinctive characteristics of, and the key differences between, the 13 named industries, and then identify the financial metrics that are most indicative of those traits.

The case examines the development and launch of an exchange-traded fund (ETF) based on JUST Capital's socially responsible corporate ranking methodologies. The case provides a market overview of Environment, Social, and Corporate Governance (ESG) and socially responsible investing (SRI), what has driven growth in those areas worldwide, and several best-practice investment approaches. Following the overview, the case describes the founding and development of JUST Capital, explores JUST Capital's ranking methodologies, and presents the decision point faced by the CEO: requisite selection of one of three strategies in order for JUST Capital to generate “self-sustaining” revenue.

Allocation of the marketing budget has become increasingly challenging due to the diverse channel exposure to customers. This study aims to enhance global marketing knowledge by introducing an ensemble attribution model to optimize marketing budget allocation for online marketing channels. As empirical research, this study demonstrates the supremacy of the ensemble model over standalone models.

The transactional data set for car insurance from an Indian insurance aggregator is used in this empirical study. The data set contains information from more than three million platform visitors. A robust ensemble model is created by combining results from two probabilistic models, namely, the Markov chain model and the Shapley value. These results are compared and validated with heuristic models. Also, the performances of online marketing channels and attribution models are evaluated based on the devices used (i.e. desktop vs mobile).

Channel importance charts for desktop and mobile devices are analyzed to understand the top contributing online marketing channels. Customer relationship management-emailers and Google cost per click a paid advertising is identified as the top two marketing channels for desktop and mobile channels. The research reveals that ensemble model accuracy is better than the standalone model, that is, the Markov chain model and the Shapley value.

To the best of the authors’ knowledge, the current research is the first of its kind to introduce ensemble modeling for solving attribution problems in online marketing. A comparison with heuristic models using different devices (desktop and mobile) offers insights into the results with heuristic models.