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Content marketing is a crucial aspect of digital marketing in modern firms. By generating content that is interesting and engaging, companies have the two-fold advantage of promoting their products in a relatable way, while increasing familiarity and engagement with the brand. As data scientists at Credit Suisse, we value our content teams because their voice is the bank's voice. We strive to provide them with the best tools to increase their articles' success. With the help of machine learning, we have created digital products that allow them to improve articles before publication, recommend them to the most interested readers, and track their performance. The chapter begins with a brief introduction to content marketing, followed by an overview of our data, a review of the business challenges we have encountered, and the machine learning solutions we have developed in order to provide the best data insights to our internal and external stakeholders. We close the chapter with a brief summary of our work.

In the rapidly changing business world, innovation plays a vital role for organizations to gain a competitive advantage. Various factors associated with technology management and innovations in organizations are diverse in the existing literature. Therefore, there is a need to bridge these gaps in the fitting proportions toward innovations within organizations. The primary objective of this study is to identify, explain and interpret the relationships between the identified technology-related factors that are important for innovations in organizations.

In this study, a modified total interpretive structural modeling (M-TISM) methodology was used to examine and analyze the various interactions between identified factors for innovations in organizations. However, the argumentation of the links is relatively weak in M-TISM. In order to compensate for this, M-TISM is additionally altered by an “Argumentation-based Modified TISM”. Hence, this research strengthens the modified TISM methodology by incorporating argumentation and total interpretation of the relationships between the identified factors.

A total of six major factors were identified using a literature review. Results suggest that workforce technical skills, technological infrastructure, technological alliances, technology transfer and top management support have an impact on innovation in organizations. Results also suggest that top management support and the technological infrastructure of an organization have a greater impact on innovation.

For policymakers and practitioners, this study provides a suggestive list of critical factors, which may help to develop policies or guidelines for improving innovation in organizations. Policymakers should focus on technological infrastructure and collaborations to enhance innovations and productions within the organizations. For academicians, this study provides a modified TISM model that shows the impact of technology-related factors on innovations. Future researchers could expand this study by adding a greater number of technological factors and validate this model in other industries.

This study fills a gap in the literature by interpreting the various relationships among the identified factors and innovations. The model has been validated through a panel of seven experts from the Indian automotive industry of multiple organizations. This study is useful in the automobile industry as it determines what and how technology-related factors affect innovations, process improvement and R&D production for organizations.

This paper examines the innovation behavior of family-owned firms versus non-family-owned firms. The role of internal family governance and the influence of external stimuli (competition) on innovation are also considered.

The data of 20,995 family and non-family firms across 38 countries are derived from the World Bank Enterprise Survey during the period 2019–2020. Probit models are used to examine the impact of family ownership, family governance, and competition on innovation outcomes.

Family firms are more likely to make R&D investments, acquire external knowledge, engage in product innovation (including innovations that are new to the market) and process innovation, relative to non-family firms. However, a high propensity of family member involvement in top management positions can reduce innovation. Competition has a negative impact on innovation outcomes for both family and non-family firms, but it has a positive moderating effect on the innovation activities of family firms where a higher level of family member involvement in management is present.

This paper provides novel insights into family firm innovation dynamics by identifying family firms as more innovative than non-family firms for all types of indicators, debunking the idea that family firms are conservative, reluctant to change, and averse to the risks in innovation activities. However, too much family involvement in decision making may stifle some innovation activities in family firms, except in cases where the operating environment is highly competitive; this provides new insights into the ownership-management dynamic of family firms.