Doing Good across Organizational Boundaries: Sustainable Supply Chain Practices and Firms’ Financial Risk

Purpose – The purpose of this paper is to theoretically hypothesise and empirically test the impact of sustainable supply chain practices (SSCPs) on firms’ financial risk. Design/methodology/approach – This research adopts signalling theory to explain the signalling role of SSCPs and the moderating role of the signalling environment in terms of supply chain characteristics. It collects and combines longitudinal secondary data from multiple sources to test the direct impact of SSCPs on firms’ financial risk and the moderating role of supply chain complexity and efficiency. It conducts various additional tests to check the robustness of the findings and to account for alternative explanations. Findings – This research shows that SSCPs help firms reduce financial risk but do not affect their returns. Moreover, the risk reduction of SSCPs is greater for firms with more complex and efficient supply chains. The findings are robust to alternative variable measurements and analysing strategies. Research limitations/implications – This research reveals the role of SSCPs in reducing financial risk, urging researchers to pay more attention to the financial risk implications of supply chain practices in general and SSCPs in particular. Practical implications – This research encourages firms to engage in SSCPs to reduce financial risk and enables them to assess the urgency of their SSCPs investments in view of the complexity and efficiency of their supply chains. Originality/value – This is the first research examining the impact of SSCPs on financial risk, based on longitudinal secondary data and signalling theory. The empirical evidence documented and the theoretical perspective adopted offer important implications for future practice and research on SSCPs

The Impact of 3D Printing Implementation on Stock Returns: A Contingent Dynamic Capabilities Perspective

Purpose – The purpose of this paper is to theoretically hypothesize and empirically test the impact of 3D printing (3DP) implementation on stock returns. It further explores how the stock returns due to 3DP implementation vary across different industry environments. Design/methodology/approach – This paper integrates the dynamic capabilities view with contingency theory to provide a contingent dynamic capabilities (CDC) perspective on 3DP implementation. It argues that implementing 3DP enables firms to enhance their manufacturing capabilities and gain a competitive advantage, but the extent to which the competitive advantage can be realized is contingent on the fit between 3DP-enhanced manufacturing capabilities and firms’ operating environments. Those arguments are tested based on an event study of 232 announcements of 3DP implementation made by U.S. publicly listed firms between 2010 and 2017. Findings – The event study results show that firms implementing 3DP gain higher stock returns compared with their non-implementation industry peers over two years after the implementation. Such stock returns due to 3DP implementation are more pronounced for firms operating in more munificent, more dynamic, and less competitive industry environments. Those findings are consistent with our CDC perspective. Originality/value – This is the first research empirically examining the impact of 3DP implementation on stock returns. It provides important implications for managers to implement 3DP to enhance firms’ manufacturing capabilities and for researchers to study 3DP implementation from the CDC perspective

The use of social media in different phases of the new product development process: a systematic literature review

Using social media is high on the list of priorities for many firms looking to enhance their innovation performance in the different phases of the new product development (NPD) process. Mirroring this rising practical importance of using social media for NPD, scholars have presented a diverse range of perspectives and underscored the need for a systematic literature review. Accordingly, this study reviews 110 papers from 2002 to 2023, to synthesize the use of social media across three phases: discovery, development, and launch. Our analysis identifies nine NPD objectives that social media addresses and discusses challenges encountered. Building on this analysis, we develop an organizing framework to guide practitioners on how to adopt social media to achieve better NPD performance and propose directions for future research.</jats:p

The impact of artificial intelligence adoption for business-to-business marketing on shareholder reaction: A social actor perspective

While AI applications are becoming ever more important in B2B marketing operations, there is a lack of research to examine whether and how shareholders react to firms' AI-enabled B2B marketing initiatives. Accordingly, the purpose of this study is to explore this process by theoretically building on the social actor perspective of the firm and investigating the impact of AI-enabled B2B marketing initiatives on shareholder reaction measured by abnormal stock returns. By adopting a propensity score matching (PSM) method to generate an artificial control group of firms without adopting AI-enabled B2B marketing initiatives, we conduct an event study based on 174 sample firms (87 treatment firms and 87 matched control firms) publicly listed in the US between 2011 and 2020. The test results suggest that firms implementing AI for B2B marketing receive greater stock returns than their industry peers without AI implementation. In addition, the stock return is more remarkable for firms operating in turbulent environments and with less complex customer bases. A qualitative focus group discussion was conducted to further complement and enrich the findings. This study provides the first empirical evidence regarding the shareholder reaction to AI-enabled B2B marketing initiatives. The results reveal the significance of the fit between AI-enabled B2B marketing values and firms' business environments. It encourages future studies to investigate AI implementation from the social actor perspective