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This paper is an exploratory study of one approach to incorporating situational information into information retrieval systems, drawing on principles and methods of discourse linguistics. A tenet of discourse linguistics is that texts of a specific type possess a structure above the syntactic level, which follows conventions known to the people using such texts to communicate. In some cases, such as literature describing work done, the structure is closely related to situations, and may therefore be a useful representational vehicle for the present purpose. Abstracts of empirical research papers exhibit a well‐defined discourse‐level structure, which is revealed by lexical clues. Two methods of detecting the structure automatically are presented: (i) a Bayesian probabilistic analysis; and (ii) a neural network model. Both methods show promise in preliminary implementations. A study of users' oral problem statements indicates that they are not amenable to the same kind of processing. However, from in‐depth interviews with users and search intermediaries, the following conclusions are drawn: (i) the notion of a generic research script is meaningful to both users and intermediaries as a high‐level description of situation; (ii) a researcher's position in the script is a predictor of the relevance of documents; and (iii) currently, intermediaries can make very little use of situational information. The implications of these findings for system design are discussed, and a system structure presented to serve as a framework for future experimental work on the factors identified in this paper. The design calls for a dialogue with the user on his or her position in a research script and incorporates features permitting discourse‐level components of abstracts to be specified in search strategies.

Built upon customer engagement marketing theory and uses and gratification theory, this study examines the link between individual social media marketing (SMM) performance indicators and restaurant sales performance at the firm level. Moreover, the study investigates the moderating effect of advertising expenditure on this proposed relationship.

Random effect regression models were developed in Stata to examine the associations between SMM performance indicators, advertising expenditure, and restaurant firm revenue. Twelve years of SMM data from brands' Facebook pages were collected with a web scraper built in Python. Natural language processing was used to analyze the sentiment of user-generated content (UGC).

The results suggest that restaurant annual sales revenue increases as the volume of brand posts, “like”s, “share”s and positive comments on restaurants' Facebook pages increase. However, the total number of comments and the number of negative comments show non-significant associations with revenue. Firm advertising expenditure negatively moderates the relationships between sales revenue and the number of “like”s, “share”s, total comments and positive comments.

Restaurants benefit from making frequent posts on SNSs. Promotions that motivate online users to “like”, share, and comment on brand posts should be implemented. Firms with limited advertising budgets are encouraged to actively create buzz on SNSs due to evidenced stronger effects of UGC on sales performance than large advertisers.

This research bridges the gap by studying the effects of individual SMM performance indicators on restaurant financial outcomes. The findings support the effectiveness of SMM; and, for the first time, demonstrate that SMM could generate a more profound impact for firms with low advertising budgets.

This article aims to investigate and model the effects of welding-generated thermal cycle on the resulting residual stress distribution and its role in the initiation and propagation of fatigue failure in thick shaft sections.

Experimental and numerical techniques were applied in the present study to explore the relationship(s) between welding residual-stress distribution and fatigue failure characteristics in a hydropower generator shaft. Experimental techniques included stereomicroscopy, optical and scanning electron microscopy (SEM), chemical analysis and mechanical testing. Finite element modelling (FEM) was used to model the shaft welding cycle in terms of thermal (temperature) history and the associated development of residual stresses within the weld joint.

Experimental analyses have confirmed the suitability of the used material for the intended application and confirmed the failure mode to be low cycle fatigue. The observed failure characteristics, however, did not match with the applied loading in terms of design stress levels, directionality and expected crack imitation site(s). FEM results have revealed the presence of a sharp stress peak in excess of 630 MPa (about 74% of material's yield strength) around weld start point and a non-uniform residual stress distribution in both the circumferential and through-thickness directions. The present results have shown very close matching between FEM results and observed failure characteristics.

The present article considers an actual industrial case of a hydropower generator shaft failure. Present results are valuable in providing insight information regarding such failures as well as some preventive design and fabrication measures for the hydropower and other power generation and transmission sector.

The presence of the aforementioned stress peak around welding start/end location and the non-uniform distribution of residual-stress field are in contrast to almost all published results based on some uniformity assumptions. The present FEM results were, however, the only stress distribution scenario capable of explaining the failure considered in the present research.