Project suspensions and failures in new product development : returns for entrepreneurial firms in codevelopment alliances

Entrepreneurial biotech and large pharmaceutical firms often form alliances to co2develop new products. Yet new product development (NPD) is fraught with challenges that often result in project suspensions and failures. Considering this, how can firms increase the chances that their co2development alliances will create value? To answer this question, the authors build on insights from signaling theory to argue that prior project suspensions provide positive signals leading to an increase in value creation, while project failures have the opposite effect. In addition, drawing on insights from temporal construal theory, this research predicts that the strength of these effects is contingent on the stage along the exploration2exploitation continuum at which the alliance is formed. The authors undertook event study analyses of 248 alliances formed by 104 biotechnology firms from the US and Europe listed on eight stock exchanges over an eight2year period between 1996 and 2003. The results confirm that prior NPD project suspensions have a stronger value creation effect (or a weaker value destruction effect) in the case of exploration alliances in the upstream of NPD processes than in the case of moderate2scale exploitation alliances in the downstream of NPD. This study is among the first to examine how both prior NPD project suspensions and failures of firms affect the abnormal returns achieved from co2 development alliances. This research therefore contributes to the innovation literature by honing a better understanding of setbacks and failures in NPD. Moreover, the findings contribute to the literature on strategic alliances by identifying new conditions under which firms can create or preserve value. Third, this research contributes to signaling theory by providing evidence of the moderation effect caused by the signaling environment. Finally, this study contributes to the entrepreneurial literature on value creation for entrepreneurial firms in alliances following adverse events

Composite FORCE learning of chaotic echo state networks for time-series prediction

Echo state network (ESN), a kind of recurrent neural networks, consists of a fixed reservoir in which neurons are connected randomly and recursively and obtains the desired output only by training output connection weights. First-order reduced and controlled error (FORCE) learning is an online supervised training approach that can change the chaotic activity of ESNs into specified activity patterns. This paper proposes a composite FORCE learning method based on recursive least squares to train ESNs whose initial activity is spontaneously chaotic, where a composite learning technique featured by dynamic regressor extension and memory data exploitation is applied to enhance parameter convergence. The proposed method is applied to a benchmark problem about predicting chaotic time series generated by the Mackey-Glass system, and numerical results have shown that it significantly improves learning and prediction performances compared with existing methods.Comment: Submitted to 2022 Chinese Control Conferenc

Spinning straw into gold : innovation recycling, innovation sourcing modes, and innovation ability in Sub-Saharan Africa

As innovation is inherently risky and uncertain, it is common for firms to suspend or abandon new product/service development projects that cannot achieve pre-defined objectives. Multiple cases exist where firms have attempted to resume the development of an innovative product or service after previously suspending or abandoning it prior to completion. Research on this important innovation recycling activity is surprisingly scarce, despite its critical role in mitigating risk in the context of high environmental uncertainty. We draw our inferences from Sub-Saharan Africa (SSA), where innovation resources are relatively limited and environmental uncertainty and institutional voids prevail, a context that encourages the use of innovation recycling. This study examines how innovation recycling influences a firm's innovation ability and the moderating impact of innovation sourcing modes using a knowledge-based view of the firm and arguments from transaction cost economics. We retrieved data from the World Bank Enterprise Survey and the Innovation Follow-up Survey of 1076 firms located in eight SSA countries (Ghana, Malawi, Namibia, South Sudan, Sudan, Tanzania, Uganda, and Zambia) spanning from 2011 to 2014 to test our conceptual model. Our findings show that (1) innovation recycling has a positive influence on a firm's innovation ability and (2) this relationship is moderated by different innovation sourcing modes. These findings enrich the theory and imply that firms operating in developing countries need to develop innovation recycling by focusing on sourcing knowledge within, rather than across, firm boundaries.publishedVersio

More than Classification: A Unified Framework for Event Temporal Relation Extraction

Event temporal relation extraction~(ETRE) is usually formulated as a multi-label classification task, where each type of relation is simply treated as a one-hot label. This formulation ignores the meaning of relations and wipes out their intrinsic dependency. After examining the relation definitions in various ETRE tasks, we observe that all relations can be interpreted using the start and end time points of events. For example, relation \textit{Includes} could be interpreted as event 1 starting no later than event 2 and ending no earlier than event 2. In this paper, we propose a unified event temporal relation extraction framework, which transforms temporal relations into logical expressions of time points and completes the ETRE by predicting the relations between certain time point pairs. Experiments on TB-Dense and MATRES show significant improvements over a strong baseline and outperform the state-of-the-art model by 0.3\% on both datasets. By representing all relations in a unified framework, we can leverage the relations with sufficient data to assist the learning of other relations, thus achieving stable improvement in low-data scenarios. When the relation definitions are changed, our method can quickly adapt to the new ones by simply modifying the logic expressions that map time points to new event relations. The code is released at \url{https://github.com/AndrewZhe/A-Unified-Framework-for-ETRE}

Unlocking service provider excellence : expanding the touchpoints, context, qualities framework

Customer reviews offer scope for better understanding the customer experience (CX), which may be leveraged to improve firms' CX performance. We extend the Touchpoints, Context, Qualities (TCQ) nomenclature by integrating it with the ARC value-creation elements and the multiple dimensions of CX. Our extended TCQ framework comprises nine building blocks to delineate dynamic what we term CX performance trajectories. We test our framework by collecting verbatim text-based reviews, and transforming them into two robust data sets (weekly, and monthly), which we examine using a dynamic Hidden Markov Model. We identify three levels of CX performance states and the migrations paths between them. We find that the building blocks coherently express mechanisms that are effective at the weekly and monthly levels for helping firms improve, and prevent deterioration of, CX performance. This research enriches the CX and TCQ literature. In particular, we derive actionable guidance for managers to facilitate the dynamic management of their firm’s CX performance

Exploring online consumer review-management response dynamics : a heuristic-systematic perspective

Although the effects of managerial responses (MRs) on subsequent customer reviews (CRs) has been explored, we lack a comprehensive theoretical framework to explain the interdependent relationships between previous and subsequent CRs—specifically the dynamic influences of MRs on future CRs. We draw on emotional contagion and regulation theories to develop a heuristic systematic model to explain CR-MR dynamics in online settings. We propose six systematic processing and three heuristic processing routes to delineate the determination and persuasion effects between previous and subsequent consumers' CRs. The systematic routes describe how current customers' compliments, complaints, and emotions influence their current rating scores. The heuristic processing routes describe how previous customers' rating scores and emotions influence current customers' rating scores and emotions. We suggest MR strategies to regulate these effects. The presence and length of MRs defines the numeric heuristic route while the positive-emotion heuristic route is conceptualized through expressions of thanks, sincerity, interaction, and complimenting customers. Expressions of apology, explanation, empathy, and remedy inform the negative-emotion heuristic route. We collect text from customers' reviews and managers' responses from the TripAdvisor website using text-mining techniques and analyze our hypotheses using Pooled Ordinary Least Squares (pooled OLS) and Generalized Method of Moment (GMM) modeling. Our findings not only enrich the theoretical underpinnings of the CR/MR literature, but also provide managerial guidance on how customers' emotional contagion and rating behaviors might be regulated

Detection of Staphylococcus aureus virulence gene pvl based on CRISPR strip

IntroductionStaphylococcus aureus (S. aureus) is a prominent pathogen responsible for both hospital-acquired and community-acquired infections. Among its arsenal of virulence factors, Panton-Valentine Leucocidin (PVL) is closely associated with severe diseases such as profound skin infections and necrotizing pneumonia. Patients infected with pvl-positive S. aureus often exhibit more severe symptoms and carry a substantially higher mortality risk. Therefore, it is crucial to promptly and accurately detect pvl-positive S. aureus before initiating protective measures and providing effective antibacterial treatment.MethodsIn this study, we propose a precise identification and highly sensitive detection method for pvl-positive S. aureus based on recombinase-assisted amplification and the CRISPR-ERASE strip which we previously developed.ResultsThe results revealed that this method achieved a detection limit of 1 copy/μL for pvl-positive plasmids within 1 hour. The method successfully identified all 25 pvl-positive and 51 pvl-negative strains among the tested 76 isolated S. aureus samples, demonstrating its concordance with qPCR.DiscussionThese results show that the CRISPR-ERASE detection method for pvl-positive S. aureus has the advantages of high sensitivity and specificity, this method combines the characteristics of recombinase-assisted amplification at room temperature and the advantages of ERASE test strip visualization, which can greatly reduce the dependence on professional laboratories. It is more suitable for on-site detection than PCR and qPCR, thereby providing important value for rapid on-site detection of pvl