Traveling salespeople usually play an integral part in exporting firms’ strategy. However, the Covid-19 pandemic and travel restrictions have forced many businesses to rely on digital communication technologies for communication with distant customers and partners they usually would meet with in person. For collaborative relationships to function well, both parties must be able to understand and make sense of each other, which is why the sensemaking process has been a topic of interest. An aspect laid forth by research as a potentially prominent issue in inter-cultural relationships is ambiguity and misunderstandings, for which previous research has underlined two main mechanisms to mitigate ambiguity and misunderstandings, formalization, and socialization. However, this research has been conducted in a context where both in-person and digital communication has been available options. This qualitative study aims to examine export sellers' perception of sensemaking, misunderstandings, and ambiguity when relying on digital communication through semi-structured interviews. This paper's result suggests that digital communication tended to be more formal than in-person communication and that relying solely on digital communication has imposed significant challenges for establishing a new relationship while being less problematic to maintaining relationships.

To gain a better understanding of intercultural communication, it is relevant to study speech acts not only in different languages but also across different language varieties. Seeing as speech act studies are said to have suffered from Anglocentrism there is a necessity to include nonwestern cultures (Wierzbicka, 1985:145). The current study seeks to gain an understanding of the understudied speech act of congratulations in two different varieties of English (American and Indian). In addition, to gain further insights and move away from the aforementioned Anglocentrism, Peninsular Spanish will also be investigated. The questions that have guided this research are: 1) What type of congratulation strategies do the respondents report using? 2) To what extent do the variables of power and social distance seem to influence the congratulation performance? 3) To what extent are there similarities and differences between the respondents with respect to the reported congratulation strategies? To identify the strategies, a modified version of Elwood’s (2004) taxonomy on congratulations was used. Data was gathered through Discourse Completion Tests (DCT) completed by 90 respondents from North America, India and Spain offering congratulations in nine different situations. The results indicated that different strategies are applied by the groups depending on situation and/or variables. For instance, North Americans and Spaniards are more likely to express happiness and Indians are more likely to offer good wishes.

Of the many existing eukaryotic gene finding software programs, none are able to guarantee accurate identification of genomic protein coding regions and other biological signals central to pathway from DNA to the protein. Eukaryotic gene finding is difficult mainly due to noncontiguous and non-continuous nature of genes. Existing approaches are heavily dependent on the compositional statistics of the sequences they learn from and are not equally suitable for all types of sequences. This thesis firstly develops efficient digital signal processing-based methods for the identification of genomic protein coding regions, and then combines the optimum signal processing-based non-data-driven technique with an existing data-driven statistical method in a novel system demonstrating improved identification of acceptor splice sites. Most existing well-known DNA symbolic-to-numeric representations map the DNA information into three or four numerical sequences, potentially increasing the computational requirement of the sequence analyzer. Proposed mapping schemes, to be used for signal processing-based gene and exon prediction, incorporate DNA structural properties in the representation, in addition to reducing complexity in subsequent processing. A detailed comparison of all DNA representations, in terms of computational complexity and relative accuracy for the gene and exon prediction problem, reveals the newly proposed ?paired numeric? to be the best DNA representation. Existing signal processing-based techniques rely mostly on the period-3 behaviour of exons to obtain one dimensional gene and exon prediction features, and are not well equipped to capture the complementary properties of exonic / intronic regions and deal with the background noise in detection of exons at their nucleotide levels. These issues have been addressed in this thesis, by proposing six one-dimensional and three multi-dimensional signal processing-based gene and exon prediction features. All one-dimensional and multi-dimensional features have been evaluated using standard datasets such as Burset/Guigo1996, HMR195, and the GENSCAN test set. This is the first time that different gene and exon prediction features have been compared using substantial databases and using nucleotide-level metrics. Furthermore, the first investigation of the suitability of different window sizes for period-3 exon detection is performed. Finally, the optimum signal processing-based gene and exon prediction scheme from our evaluations is combined with a data-driven statistical technique for the recognition of acceptor splice sites. The proposed DSP-statistical hybrid is shown to achieve 43% reduction in false positives over WWAM, as used in GENSCAN.

Of the many existing eukaryotic gene finding software programs, none are able to guarantee accurate identification of genomic protein coding regions and other biological signals central to pathway from DNA to the protein. Eukaryotic gene finding is difficult mainly due to noncontiguous and non-continuous nature of genes. Existing approaches are heavily dependent on the compositional statistics of the sequences they learn from and are not equally suitable for all types of sequences. This thesis firstly develops efficient digital signal processing-based methods for the identification of genomic protein coding regions, and then combines the optimum signal processing-based non-data-driven technique with an existing data-driven statistical method in a novel system demonstrating improved identification of acceptor splice sites. Most existing well-known DNA symbolic-to-numeric representations map the DNA information into three or four numerical sequences, potentially increasing the computational requirement of the sequence analyzer. Proposed mapping schemes, to be used for signal processing-based gene and exon prediction, incorporate DNA structural properties in the representation, in addition to reducing complexity in subsequent processing. A detailed comparison of all DNA representations, in terms of computational complexity and relative accuracy for the gene and exon prediction problem, reveals the newly proposed ?paired numeric? to be the best DNA representation. Existing signal processing-based techniques rely mostly on the period-3 behaviour of exons to obtain one dimensional gene and exon prediction features, and are not well equipped to capture the complementary properties of exonic / intronic regions and deal with the background noise in detection of exons at their nucleotide levels. These issues have been addressed in this thesis, by proposing six one-dimensional and three multi-dimensional signal processing-based gene and exon prediction features. All one-dimensional and multi-dimensional features have been evaluated using standard datasets such as Burset/Guigo1996, HMR195, and the GENSCAN test set. This is the first time that different gene and exon prediction features have been compared using substantial databases and using nucleotide-level metrics. Furthermore, the first investigation of the suitability of different window sizes for period-3 exon detection is performed. Finally, the optimum signal processing-based gene and exon prediction scheme from our evaluations is combined with a data-driven statistical technique for the recognition of acceptor splice sites. The proposed DSP-statistical hybrid is shown to achieve 43% reduction in false positives over WWAM, as used in GENSCAN.