As no study on attitudes towards local food has compared “organic” and national quality scheme consumer segments, this study aimed to provide further insights and clarifications on the issue of consumer segmentation in terms of trust towards organic food and food of selected quality perceived as local, along socioeconomic characteristics, and other important determinants of this complex interaction. The research examines consumers’ attitudes and perceptions related to two quality schemes for special Slovenian foods: “Organic”, which relates to production methods; and “Selected Quality”, which relates to quality attributes. The study focused on two segments of consumers, who exhibit a high level of trust towards the two quality schemes. Comparative analysis of the consumer segments looked for the potential differences with respect to their sociodemographic profiles, as well as to their understanding of the definition of local food, attitudes towards local food, trust in actors and institutions, and willingness to purchase local food. The study combined qualitative approaches and a quantitative survey with a general population sample. The results showed that both consumer groups have similar understandings of local food, with region-based interpretations outperforming country-based interpretations. The “Organic” group was more cosmopolitan and supportive of the local community, regardless of geographic proximity, than the “Selected Quality” group. Older consumers occupy a larger share of both segments, with professionals and individuals with higher incomes more likely to be in the “Organic” group and retirees and students more likely to be in the “Selected Quality” group. To increase the consumers’ interest in food with the “Organic” and “Selected Quality” schemes, more specific product propositions should be developed. Food quality certifications are gaining importance in shaping local and global agricultural and food systems . Such scAdditionally, most EU member states and many regions, as well as private providers, are implementing national QACS in the above-mentioned category of voluntary certification schemes ,3, also There is growing evidence that food consumption preferences for organic food and food of local origin are related in the sense that consumers who value locally produced food have preferences for organic food ,5,6, whiIn the context of reducing the information asymmetry, trust—defined as “a psychological state that involves the intention to accept vulnerability based on positive expectations of the intentions or behaviors of another” —can be aHaving these challenges in mind, this study intends to contribute to further knowledge about the awareness, understanding, discrimination, perception, and evaluation of two categories of QACS: namely, the “European/national scheme for organic farming” (“Organic” scheme) and the voluntary national scheme “Selected Quality”. The study includes several research questions related to the existence of differences in the sociodemographic profile among the two segments of consumers, namely: (1) “trust in the Organic food label group” and (2) “trust in the national quality scheme Selected Quality”, and differences in the understanding of the definition of local food, attitudes towards local food, trust in actors and institutions/companies, and willingness to buy local food. Consumers’ attitudes towards local food, and their trust and purchasing habits will also be investigated in relation to trust towards the “Organic” and “Selected Quality” schemes and compared against the behavior of the general population sample. This paper thus aims to provide further insights and clarifications on the issue of consumer segmentation in terms of preferences for organic food or food of local origin, along socioeconomic characteristics, and other important determinants of this complex behavioral phenomenon. Undoubtedly, a comprehensive understanding of this phenomenon is essential for more effective planning of marketing strategies by business actors, and policy design and implementation by public authorities.The study is based on qualitative and quantitative approaches to identify possible significant patterns of differences between the two consumer segments studied and compare them with the general, nationally representative population. In the first phase, focus groups were conducted with participants selected according to gender, age, education, income, and place of residence  using the snowball method. The purpose was to explore the relationships between consumer opinions and consumption motives. The focus groups included 8–10 persons of different sociodemographic backgrounds. They were conducted with prior consent of the participants in a video conferencing format (Microsoft Teams software) during the winter of 2020/2021, as COVID-19 preventative measures prevented us from conducting live focus groups. The focus group discussions, which lasted about 2 h on average, focus on three main topics: (1) factors influencing the consumption of certified “Organic” and “Selected Quality” labeled foods, (2) consumer preference for certified “Organic” and “Selected Quality” labeled foods, and (3) the ranking of these attributes to determine their relative importance. The results were further tested in a real-life situation with a subset of 16 participants through the method of shopping with consumers to identify direct experiences in the retail environment . The actThe quantitative data collection was conducted using an on-line questionnaire. Subjects were recruited from a consumer panel of a marketing research institute, which consists of about 35,000 subjects from Slovenia. We used quota sampling for age groups, gender, and place of residence to ensure that the sample structure is comparable with the Slovenian population. The study sample included 1007 participants, aged between 18 and 65. All the data was collected in May and June 2021. The study design of the quantitative survey included two interventions in the original sample, which was randomly divided into two equal samples that controlled for age, gender, and place of residence. Each participant of the two samples was asked to identify the five most important attributes associated with certified organic foods and food with the national quality scheme label “Selected Quality”. Additionally, the original sample was randomly divided into five subsamples, using quotas for age, gender, and place of residence. In each subsample, the respondents were asked to indicate the importance of various attributes when purchasing local foods in five different food categories. All respondents were asked to answer all survey questions, considering the areas of interest specified by the study design.The draft questionnaire was based on existing academic literature on the topic, focus group discussions, and the results of shopping with consumers. It was pretested by six leading Slovenian food experts and checked for correct transcription and comprehension in a pilot study with over 100 participants.The participants were asked to complete a self-administered structured online questionnaire with six blocks. The first block dealt with sociodemographic factors ). The second block focused on understanding the definition of local food, which was measured using scales adopted from previous studies ,17,23. FData from the focus groups and shopping with consumers was analyzed using thematic analysis, the most common analysis for qualitative data used to find common patterns in a database . The codThe responses to the questionnaire were analyzed using the Statistical Package for Social Sciences version 27.0 (IBM SPSS Statistics), and STATA version 17.0 (StataCorp LLC). Prism version 9.1.0  was used for developing graphs. Before proceeding with the analysis, the data was screened for missing values, outliers, and normality. Further, they were tested against all assumptions of the statistical methods used. To investigate the importance respondents assign to a list of initiatives/barriers in their purchase decisions for the “Organic” and national “Selected Quality” label, we used the Borda count positional aggregation method . The mett-test was employed to validate the participants’ group classification.Based on the respondents’ choice of trust as top important determinant for food purchases under the analyzed quality systems , two consumer segments were developed, namely “I trust the Organic label” and “I trust the Selected Quality label”. To validate this classification, a separate question was asked, investigating how much respondents’ trust to quality labels, measured using a 7-point Likert-type scale. A mean comparison analysis of both groups against the whole sample using the two-samples independent t-test for equality of means were employed to detect the differences between groups. Additionally, multiple correspondence analysis was used to identify the position of two quality systems trustors in relationship to the socioeconomic characteristics. Additionally, the importance of the different food attributes for the identified consumer groups was analyzed. To create an importance scale, the percentage in which an attribute was reported as least important was subtracted from the percentage in which it was reported as most important, resulting in a measure that ranged from 100% to −100% [Descriptive statistics were used to describe the sample and segments regarding their sociodemographic characteristics. The one-sided test of equality for column proportions and the one- and two-sided to −100% ,28.Exploratory factor analysis (EFA) with orthogonal varimax rotation was conducted to determine the principal factors underlying respondents’ understanding, perception, trust, willingness to purchase, and interest in information cues denoting quality, traceability, sustainability, and origin. The latent variables scale internal consistency was measured using Cronbach’s alpha and is presented in the t-test was conducted to determine significant differences between the groups’ factor scores. Unless otherwise noted, any p-value < 0.05 was considered statistically significant.Multivariate analysis of variance (MANOVA) was conducted to determine the variability between the groups with respect to the latent variables’ measures. A subsequent CI = 0.08 − 0.62, p = 0.0057). The same was observed for the group “I trust the Organic label”, in comparison with the whole sample, which showed a lower level of trust in the “Organic” label . Based on the first-ranking selection of the variable “Trust in the quality scheme”, we divided the respondents into two groups, namely “I trust the Organic label” and “I trust the Selected Quality label”. The segment size of the two trustor groups was comparable, with the “I trust the Organic label” group representing 9.63% and the “I trust the Selected Quality label” group representing 10.43% of the total analyzed sample. The result of the validation analysis using the statement “I trust quality labels” of “I buy meat from a local farmer I know and salami from producers with the “Selected Quality” label because they taste almost the same as when I was a kid. Local food is good and healthy, everything else is artificial products”.The position closeness of the two segments in relationship to their sociodemographic characteristics is additionally presented in the Notable differences were also found between the two segments in terms of occupational status, with the greater proportion of employed people likely to be in the “I trust the Organic label” group, while students were more likely to be in the “I trust the Selected Quality label” group. Focus group analysis revealed a more complex picture, with students aware of the importance of quality labels and organic foods, but viewing them as too expensive, not justifying that the specific nutritional values are important to them. A typical statement was made by a 20-year-old student, who said, “I know that these foods are good, but they are absolutely too expensive. Also, I am most interested in foods that have more protein and less carbohydrates. I cannot find these qualities in foods with these labels”.The notable differences between the two groups in terms of education were found among consumers with secondary education, with the “I trust the Organic label” group likely to be more educated, while both groups are more educated compared to the general sample. This has been confirmed by the focus group analysis, which revealed that participants who had completed the secondary school curriculum, which included the topic of food labeling, had more trust in and more positive attitudes towards foods under QACS: “Yes, we learned about food labeling in school and that local foods are better because they are healthier and more environmentally friendly” .“I know last time I said that it matters to me if it’s an animal and environmentally friendly product. Well, it’s really important to me, but I rarely have time to look at it closely. I make sure it’s seasonal, and I do not buy strawberries in February because they are artificial,” the 49-year-old employee said.Country of origin was also rated rather low among the 10 attributes, although the results of the focus group discussions indicated that it was highly important: “I get very annoyed when I buy Chianti wine that is supposedly from Florence but is actually made in China or elsewhere. That’s not right. That’s fraud,” said the 35-year-old lawyer.A closer look at the product categories  shows th“Yes, I trust this label. And I buy organic products, and I want more and more people to buy such products. This is the only right direction for rural development in the less developed parts of the world, you know. It will bring more money, which means better infrastructure development, from schools and roads to digitization around the world. And people will stay at home and not crowd into cities, which is a general trend.”Respondents in the group “I trust the Selected Quality label” are more likely to support local farmers and producers compared with the general population and show notably higher support for these actors compared with the group “I trust the Organic label”. Additionally, we observed neutral trust with no significant difference between the groups of the manufacturers and retailers that indicate production of local food and in government institutions that monitor compliance with the local production standards. In terms of the understanding of the definition of local food, an interesting trend can be seen showing a higher regional interpretation of the definition of local food in the group “I trust the Selected Quality label” compared with the group “I trust the Organic label” . In one “I do not know what is local here, in Slovenia, because we are so small. When I buy Slovenian cheese or Slovenian apples at the market, it is local for me, even if they were produced on the other side of Slovenia. On the other hand ... I do not know... I am really confused. ... It gets even more complicated with food companies. It’s unclear to me what local food businesses are. Do you mean ownership of a business or food ingredients? For me, Fructal is no longer a Slovenian company, but a Serbian one, and that’s why I do not buy their products anymore, because the money does not stay here. I am really confused.”Although neutral, a comparably more favorable attitude is observed from the general population towards multinational corporations and large retail chains, and less favorable attitudes towards the origin of products, while the other two groups are more likely to reject food produced by a multinational company and feel uncomfortable eating food with which they are not familiar. This was also confirmed by the focus group analysis. A typical statement from a 52-year-old Master of Pharmacy was: “If it’s possible, I want to know where the product comes from, that it’s not made by a multinational company that just manipulates food.” There are also differences between consumer groups that show a different willingness to buy local food. Both the “I trust the Organic label” group and the “I trust the Selected Quality label” group are significantly more willing to buy local food and look for a shop that offers local food than the general population. The focus group analysis also confirmed this result in contrast to the analysis of shopping with consumers, as all the focus group participants who stated they would buy local food did not implement this during the shopping observation, mainly because of the high price and taste. A typical statement comes from a 29-year-old fitness instructor: “Yes, I know I said that, but I have to admit that I only buy local food when it is cheaper, that is, when the price of these products is generally like those of other products. In that case, of course, I always buy a local product.”Food consumer research literature has not yet sufficiently tackled the question of similarities and differences between attitudes and expectations of organic food consumers and other consumers. The question is particularly relevant when planning marketing strategies and accompanying support activities. In this respect, it is important to understand whether consumers of organic food and consumers who rely on other QACS in the agri-food system belong to complementary or competitive market segments and how they differ from the general population. This study sought to fill this research gap by using a mixed methods approach with qualitative and quantitative analyses of food consumer preferences and behavior in Slovenia.The original contribution of this study is to provide further insight about the similarities and differences in the attributes affecting food purchase between the consumers of organic food and consumers who rely on other QACS. Our results show that the “Organic” and the national voluntary “Selected Quality” consumer groups have a similar understanding of the definition of local food, while the regional-based interpretation exceeds the country-based interpretation. However, consumers of organic food are more cosmopolitan and more likely to support the local community, regardless of geographic proximity, than consumers of food with the national “Selected Quality” label. In contrast to the study by Ditlevsen et al. , which dThe results of the analysis of the underlying dimensions of the latent variables are somewhat consistent with what the literature has shown ,16,17,23Regarding the importance of attributes, the correspondence analysis identified two dimensions that effectively distinguish the two segments and proved the suitability of trust in a quality system as a segmentation variable that can correctly classify consumers, especially those who have very high trust in organic food labeling and have proven to be those who value local food, as described in the literature . The study found that both consumers with very high trust in organic food labeling and those trusting the “Selected Quality” national scheme were more likely to purchase local foods and search for a shop that offered local foods than the consumer part of the general population. However, the analysis of shopping with consumers does not confirm this. Thus, this discrepancy suggests that declarative statements do not  find confirmation in behavior and that the use of different methods is useful to verify results based on declarative statements. The so-called attitude/value-behavior gap ,36, whicA vague understanding of the definition of local food regardless of consumer segments, as pointed out by many authors ,16,17, wTherefore, this study showed that there is a need to pay much more attention to consumers’ overall understanding of local food and their trust in local food actors, farmers, businesses, and organizations. Consumer interest and search for transparent information about food quality, traceability, origin, and sustainability should not be neglected in supporting demand for foods with quality labels and QACS. It is not appropriate to focus a communication or promotion campaign on attributes that appeal primarily to general consumers, such as seasonal ingredients, on-farm production, country of origin, animal welfare, and traditional recipes. On the contrary, the consumers with high trust in the “Organic” label would react to attributes such as: strict quality control, traceability, and region of origin; the same holds true for the consumers with high trust in the national voluntary label “Selected Quality”. Consumer education, constant and systematic promotion and communication of the QACS, and providing support to improve distribution on the markets, are needed to strengthen the economic development of the niche sector.Although the study provided valuable insights into the niche segments of the food market, this analysis is limited by the fact that it was conducted during the COVID-19 pandemic, when various restrictive measures were imposed in response to the pandemic. This also explains why focus groups and the quantitative survey were fully conducted on-line. Previous reports have highlighted that the epidemic affected consumer food-related behaviors, but it is not clear whether such changes are permanent . We alsoThe combined methods approach using shopping with consumers, qualitative focus groups, and a quantitative survey provide data that shows a very high level of agreement in the results obtained, suggesting that there are different segments of the population willing to buy local food, although they have different opinions regarding the financial value of such food. The “Organic” and national voluntary “Selected Quality” consumer groups have similar understandings of local food, with region-based interpretations outperforming country-based interpretations. The “Organic” group was more cosmopolitan and supportive of the local community, regardless of geographic proximity, than the “Selected Quality” group. Older consumers occupy a larger share of both segments, with professionals and individuals with higher incomes more likely to be in the “Organic” group and retirees and students more likely to be in the “Selected Quality” group. To increase the consumers’ interest in food with the “Organic” and “Selected Quality” schemes, more specific product propositions should be developed.


This paper presents a conceptual framework that accurately represents the current and future perspectives of data-driven companies in tourism by means of an analysis of the data sources used in the data-driven tourism research literature, as well as the research topics to which they are applied. For this purpose, a bibliometric analysis of data-driven tourism research is carried out. The framework of the study is all tourism-related publications whose research was based on data sources during the period 1982–2020. The results show some of the basic bibliometric performance indicators and the maps of science. The main themes of research interest are identified, and the conceptual evolution is obtained based on these maps. Three major thematic areas are identified: tourism research topics, information sources, and data analysis techniques. Based on these three thematic areas, the conceptual model of data architecture and processes of a data-driven organization in the tourism sector are obtained. An additional qualitative analysis of the three thematic areas is performed. Digitalization has led many organizations in different sectors of activity to transform their operations by prioritizing data as a driver of their development. This change  processes. The raw data is stored in the data lake and the processed, cleaned, and enriched data is stored in the data warehouse.Analytical techniques and BI: This component contain business intelligence and advanced analytics solutions. The advanced analytics is supported by data mining or data science of which modeling phase is, in turn, supported largely by artificial intelligence, and more specifically, by machine learning systems.Business insights: This component is related to business decisions based on the knowledge discovered in the previous component. This knowledge will be applied to improve the relationship with the customer, it will materialize in data-driven business decisions.The different components of the conceptual model are explained in more detail below:As mentioned above, this work aims to instantiate this generic conceptual architecture in the tourism sector landscape drawing what parts are actually being used in the sector in each of its layers.Some authors have conducted a review of bibliometric studies in the field of tourism  of WoS articles. It also differs in the analysis of the research themes and their conceptual evolution over the period, all using the science mapping tools provided by SciMAT  NEAR/3 (“data source*” OR “information source*” OR “dataset*” OR “information system*” OR “database*” OR “data warehouse*” OR “data lake*”))Obtaining a total of 794 documents in that time frame (1982–2021). The data were downloaded on 21/09/2022. In this research, to perform the bibliometric analysis, we have used the SciMAT software   into two periods ensuring that both periods had a similar number of documents. The data were therefore divided into two consecutive time periods: 1982–2015 with 393 papers and 2016–2021 with 401 papers.In this phase, the bibliometric analysis of performance was performed, in which publications are analyzed from multiple perspectives: evolution over time of the number of publications, journals with the highest number of publications, geographical distribution of publications, and most productive authors. Graphs obtained in this bibliometric analysis are made with Python programming language and Matplotlib package using SciMAT’s data.Next, science maps were made, which are spatial representations of the relationships that exist between documents, authors, fields, or disciplines. They have been widely used in the scientific literature in very diverse fields to show these relationships graphically , where i and j are keywords belonging to the theme, and w is the number of keywords in the theme. With these two measures, we can visualize the research themes through a two-dimensional strategic diagram , and the descriptions will be supported by tables and strategic diagrams. In the strategic diagrams, the size of the sphere of each research theme is proportional to the number of papers published on that theme.As shown in Fig. Table These main themes are described below:The “tourism-information-systems” theme is a driving theme; it is the theme with the largest number of documents, although it has less weight in terms of citations and h-index. It focuses on the sources of information used in tourism, with emphasis on sources of geographic information data (GIS), and to a lesser extent and with much less weight than the previous one, on statistical information systems. It also includes various issues that refer to technological issues related to information sources such as the semantic web, ontologies, and integration processes.Another outstanding driving theme is “performance”; this is the second theme with the highest number of citations. It focuses on issues related to measuring competitiveness and impact from different perspectives of the tourism industry, highlighting themes such as service quality and to a lesser extent productivity or strategies. The competitiveness and service quality of tourism destinations are one of the main objects of study in data-driven analyses in the tourism sector.The third leading driving theme is “travel-agencies”; it is the third theme with the highest number of citations. It includes themes related to travel agencies, the Internet, or the use of images of tourist destinations, reflecting the growing importance of the Internet in tourism, as well as the use of images of destinations. It also includes issues related to relational databases, reflecting their use for the storage and analysis of structured information in the tourism sector.The theme “customer-satisfaction” is a basic and transversal theme. It is the theme with the highest number of citations and the second-highest h-index. It covers a wide range of issues, but those related to tourist behavior, such as loyalty or motivations, stand out. Similarly, and related to the previous ones, there are issues related to the search for information. Tourist behavior and satisfaction is another of the main topic of analysis in data-driven tourism research.The “tourism” theme is a basic and transversal theme. It is the second theme with the highest h-index, equal in this position with performance. As is usual for the themes in this quadrant, it is a fairly general theme. It includes wide-ranging issues related to management or services, such as market segmentation or distribution channels.Tourism research topics: It covers this research topics from a business perspective. The themes “customer-satisfaction,” “tourism,” and “performance” focus on topics under study in data-driven tourism research and is included in this thematic area. Other themes of the period, with a weight in number of documents, h-index, and number of citations much lower than the previous ones, we find a similar classification: “perceived-risk,” “disability,” “environment,” “business,” “strategies,” and “perceived-usefulness.”Information sources: It includes themes related to information system sources. The theme “travel-agencies” and “tourism-information-system” belongs to this area. The theme “geosocial-networks” also belongs to “information sources.” We further include those related to geolocated social networks.Data analysis techniques: This area covers themes related to statistical techniques and data science that are applied in research. The “clustering” topic belongs to this area.Looking at all the themes identified in this period, it makes sense to group them into three broad thematic areas, namely:Figure As can be observed in Fig. Table These main themes are described below:The theme “online-review” is a driving theme; it is the third theme with the highest number of documents and the second in terms of citations. It focuses on information generated by the tourist, either through ratings or opinions on intermediation platforms in the contracting of tourism services, satisfaction surveys, comments on social networks, exchange of information in virtual communities, or e-mails. Data of this type are more generally classified in UGC (user-generated content) data . It is a keyword also more frequent in the second period than in the first.In first place, we find “information-sources” or “tourist-information-system” among the keywords with the highest number of documents. Also noteworthy is the keyword “GIS” which refers to the high use of spatial data in tourism research or “big-data” which reflects the use of large datasets.Reviewing the keywords in more depth, we find notable changes between the periods in the sources of information used, as we have already seen in the evolution map. For example, the keyword “online-reviews” and others with the same meaning “online-customer-reviews” or “online-travel-reviews” appear with a high frequency in the second period, while they do not appear in the first period. This keyword refers to the reviews made by tourists on online platforms for booking accommodation and tourist services , which are increasingly used as sources of information in data-driven tourism research.It is interesting to take a closer look at the “online-reviews” cluster in the second period, already mentioned in “First Period (1982–2015)” above. In Fig. As indicated above, data of the type “online-reviews” are more generally classified in the UGC (user-generated content) data. Similarly, the keyword “user-generated-content” appears in the second period, but not in the first. Something similar happens with keywords referring to social networks or images, with a notably higher usage in the second period. Other sources of information that are also growing are data related to IoT (Internet of Things), mobile device data or trends in Internet search engines, although in the latter ones the difference between the first and second period, reviewing the keywords, is not so notable. Similarly, the keyword “big-data” is clearly a second-period keyword, reflecting the increased use of large datasets in tourism research in recent years.As we have already mentioned, one of the keywords with the highest number of documents is “tourist-information-system.” In data-driven initiatives, integrated information systems are the key. In order to detect this type of systems that centralize information, we have searched for other keywords such as “data-warehouse” or “data-lake” and similar. Reviewing the documents associated with them, we found a relatively low development of this type of repositories in the tourism sector, where their use could be key.With a similar objective, we searched for keywords referring to tourism statistics data, such as “tourism-statistics” or similar. We found a relatively low number of documents, reviewing the documents we found that tourism statistics data produced by different types of institutions has a relatively low use in data-driven tourism research.We found something similar when we reviewed open data sources and searched for keywords such as “open-data.” We found a low number of documents that denotes a low use in research of this type of sources that have had a remarkable growth in the last decade.With regard to “data analysis techniques” thematic area, three themes have been detected: “clustering” in the first period and “machine learning” and “deep learning” in the second period Figs. , and 15.Keywords related to more advanced or innovative analytical techniques, such as “machine learning,” “sentiment-analysis,” “text-mining,” “natural-language-processing,” “recommendation-systems,” “sentiment-analysis,” or “deep learning” appear much more frequently in the second period, reflecting the increasing use of advanced analytical techniques in tourism research, in accordance with what was seen in the evolution map.Once the major areas have been identified and analyzed, we are now going to translate these areas into the conceptual model of data and process architecture presented in Fig. Data sources: The most commonly used data sources in tourism research are identified. The evolution of information sources from one period to the next is shown. We obtain a wide range of different sources of information, among which we can highlight: tourism information systems  to the use of a broad spectrum of sources: GIS, IoT, images, social networks, search engine trends, opinions, and comments platforms. In this regard, it is worth noting the growing importance of UGC data  and mobility data. This type of information makes it possible to analyze customer satisfaction or customer behavior. This type of analysis has been gaining importance and a transition has taken place that places the tourist at the center of the actions.In the conceptual model, a data management solutions area is proposed. Regarding the use of this type of area, it has been detected a significant lack of centralized data repositories to collect and store data on a large scale, making use of appropriate data management techniques, ranging from traditional data warehouse structures to more flexible an agile data lake. The development of such repositories would centralize and simplify access to massive data sources and extend and democratize access to data sources to all agents in the tourism value chain.Relatively, little use is detected in the studies of statistical information generated by institutions .


Most non-Māori-speaking New Zealanders are regularly exposed to Māori throughout their lives without seeming to build any extensive Māori lexicon; at best, they know a small number of words which are frequently used and sometimes borrowed into English. Here, we ask how many Māori words non-Māori-speaking New Zealanders know, in two ways: how many can they identify as real Māori words, and how many can they actively define? We show that non-Māori-speaking New Zealanders can readily identify many more Māori words than they can define, and that the number of words they can reliably define is quite small. This result adds crucial support to the idea presented in earlier work that non-Māori-speaking New Zealanders have implicit form-based  knowledge of many Māori words, but explicit semantic  knowledge of few. Building on this distinction, we further ask how different levels of word knowledge modulate effects of phonotactic probability on the accessing of that knowledge, across both tasks and participants. We show that participants’ implicit word knowledge leads to effects of phonotactic probability–and related effects of neighbourhood density–in a word/non-word discrimination task, but not in a more explicit task that requires the active definition of words. Similarly, we show that the effects of phonotactic probability on word/non-word discrimination are strong among participants who appear to lack explicit word knowledge, as indicated by their weak discrimination performance, but absent among participants who appear to have explicit word knowledge, as indicated by their strong discrimination performance. Together, these results suggest that phonotactic probability plays its strongest roles in the absence of explicit semantic knowledge. How many words of Māori can non-Māori-speaking New Zealanders actively define? Despite the fact that the Māori vocabulary size of non-Māori-speaking New Zealanders has been assessed in multiple studies, this is not known. This is because studies tend to focus on identifying words for which the semantics can be passively recognized, rather than more actively produced.proto-lexicon–a large collection of implicit memories of the forms of words and word-parts that are experienced with statistical regularity in Māori, without associated semantics. This argument crucially assumes that the implicit proto-lexicon is much larger than, and is thus not constituted by, the explicit vocabulary of Māori words that non-Māori-speaking New Zealanders can actively use. This crucial assumption has not yet been empirically tested. In this paper, we test this assumption empirically for the first time, through two experiments that assess different aspects of Māori vocabulary knowledge amongst non-Māori-speaking New Zealanders.This question is of particular current importance, given an emerging literature that shows that non-Māori-speaking New Zealanders have impressive gradient knowledge of the forms that can and do constitute Māori words. Such knowledge is argued to derive from a Te reo Māori (henceforth referred to as ‘Māori’) is a Polynesian language, and the language of the Indigenous people of New Zealand. It has a small phoneme inventory, consisting of ten consonants /p, t, k, m, n, ŋ, w, f, ɾ, h/ and five vowels /i, e, a, o, u/. All phonemes map to distinct graphemes; /ŋ/ and /f/ map to digraphs , while all other phonemes map to single characters. This mapping is bidirectional: there are no silent characters or phonological processes that complicate the mapping from graphemes to phonemes, and there is no ambiguity in the phonological structure of written forms due to a simple syllable template that lacks consonant clusters. The five vowels also have long forms, which are indicated orthographically with a macron over the vowel. The spelling system is transparent and known to New Zealanders, which facilitates the use of written stimuli in experiments designed to tap phonological knowledge.pōhutakawa, a kind of tree, and kiwi, a kind of bird) as well as Māori culture  [According to the 2018 New Zealand Census, only approximately 4% of the New Zealand population are able to speak Māori well enough to have an everyday conversation [r dance) .Knowledge of, and exposure to, Māori has been increasing among non-Māori-speaking New Zealanders in recent years, in parallel with the development of positive attitudes toward Māori both societally and individually ,5. MāoriRecent results suggest that incidental learning through everyday exposure endows New Zealanders who do not speak Māori with a ‘proto-lexicon’ of more than a thousand words or word-parts ,7. This A key assumption of the proto-lexicon claim is that non-Māori-speaking New Zealanders do not have conscious or active knowledge of the meanings of forms in their proto-lexicon. While this assumption is intuitive–New Zealanders who do not speak Māori are highly unlikely to know the meanings of over a thousand Māori words–it is not yet directly supported with empirical evidence. In this paper, we aim to fill this empirical gap by conducting a word identification task and a definition task on a single set of words, to establish the degree to which there are words that can be recognised, but not defined.The primary purpose of this paper is to assess the size of the active Māori vocabulary among typical non-Māori-speaking New Zealanders. Assessing vocabulary size has been a productive area of research in applied linguistics over the last 20 years, with a number of reliable measures being devised to ascertain the depth and breadth of vocabulary knowledge in both native and second languages ,9. This Despite these advances, we have sparse systematic data on how many Māori words the typical non-Māori-speaking New Zealander knows. Deverson  estimateIn this paper, we directly probe Māori word knowledge held by non-Māori-speaking New Zealanders, at two levels: first, at a level of passive or implicit knowledge, in an identification task; and second, at a level of active or explicit knowledge, in a definition task. We use a set of Māori words that we believe are most likely to be known by New Zealanders, which provides a good indication of the size of the typical active Māori vocabulary: if some subset of even these highly recognisable words cannot be reliably defined, then the active vocabulary cannot be large.The secondary purpose of this paper is to compare how different levels of word knowledge modulate effects on the accessing of that knowledge. Do different factors affect how someone responds to a word in an experimental situation when they explicitly know it, as opposed to when they only implicitly recognise it? To answer this question, we look at a factor that is known to affect lexical access: phonotactic probability.Phonotactic probability is a statistical encoding of gradient well-formedness that captures what words typically ‘sound’ like in a language, based on the extent to which subsequences of phonemes are evidenced across a range of words in that language. A body of work has established that speakers, listeners, and learners of a language have detailed statistical knowledge of phonotactic probabilities, which they can leverage for tasks such as segmenting words from a stream of speech –24 and jIn the context of a proto-lexicon, true lexical access–looking up a form and retrieving its meaning–is not possible as there are no form-meaning pairs. However, there is an analogue in word identification–looking up a form to see if it exists in the memory store. Oh et al.  show thaRaw data from the Word Identification task in Panther et al.  showing The findings of Panther et al.  seem to Oh et al.  and PantThe aim of this study is twofold. Primarily, we aim to assess the size of non-Māori-speaking New Zealanders’ active Māori lexicons. Secondarily, we aim to understand the effects of phonotactic cues on these participants’ word judgements, and their ability to associate semantic content with words.The paper thus has two overarching research questions:How large is the active Māori lexicon of non-Māori-speaking New Zealanders?How does phonotactic knowledge affect the contents of the active lexicon?Because we explicitly want to distinguish word-form knowledge from the semantic ability to actually define a word, we developed a two-stage approach to trying to determine the approximate vocabulary size of non-Māori-speaking New Zealanders. First, we attempted to find word-forms that they could accurately identify as being words of Māori. Then, we probed their semantic knowledge by asking them to provide definitions for a list of common Māori words. The study thus consists of two online experiments with non-Māori-speaking New Zealanders.In Experiment 1, we conduct online identification tasks asking non-Māori-speaking New Zealanders to rate their level of confidence that each stimulus is an actual Māori word or phrase (composed of two words) using a 1-to-5 scale. The list of stimuli is comprised of Māori words and Māori-like non-words (containing high-probability phonotactic sequences in Māori).After determining the list of Māori words identified by most of our participants in Experiment 1, we conduct Experiment 2, an online Māori word definition task. A primary goal of Experiment 2 is to assess non-Māori-speaking New Zealanders’ semantic knowledge of Māori. For both studies, we also assess whether phonotactic probability influences their ability to identify Māori words (Experiment 1) and associate meanings with them (Experiment 2).Participants were recruited online via Facebook and Twitter through our social networks. They needed to be adults (18 years or older), who do not speak Māori. They needed to have grown up speaking English in New Zealand, or have lived in New Zealand for at least two years, and not be speakers of te reo Māori.We removed 29 participants with non-qualifying demographics or outlying response patterns, as outlined in the supplementary materials. After removing outliers, there are 101 and 123 non-Māori-speaking New Zealander participants in Experiments 1 and 2 respectively. Those who completed Experiment 1 are not prohibited from participating in Experiment 2, which was conducted one month after finalising Experiment 1.haere mai ‘welcome’; kia ora ‘hello’; haka ‘traditional war dance’; kai ‘food’; kapa haka ‘Māori performing arts’) were verified. There were no suspicious participants detected based on this criterion.To detect any suspicious or unusable participants in Experiment 2 (Māori word definition task), every participant’s definitions of the five words rated as most familiar in Experiment 1  and the participants provided their written (online) consent to participate in this study.Phonotactic score is our representation of phonotactic probability, normalised for stimulus length. We calculated phonotactic score using a triphone-based phonotactic probability that was identified by Oh et al.  as most The neighbourhood density of a word is another factor which has been assumed to influence early word learning . The neiOh et al.  did not We include the presence of a macron in our models. While Oh et al.  did not The stimulus materials for the first experiment, which tests identification of Māori wordforms, comprise of a list of 132 Māori words frequently used in English (including nine phrases consisting of two words) and 209 Māori-like nonwords (including 18 phrases consisting of two Māori-like nonwords).  and ako (‘to learn’). The non-words were generated using a pseudoword generator [The Māori words and phrases were selected impressionistically as being some of those which might be most familiar to participants. For example, we included the words for the numbers 1–10, basic colour words and some commonly used words for body parts. Also included were common greetings like enerator .phonotactic score. We designed the experiment such that there would be more nonwords than real words in the stimulus set , but wished to avoid making the experiment too long for participants. Thus, for each stimulus category of real words (of size N), we selected 1.5*N non-words. Within each category, we selected non-words with the highest phonotactic scores, so as to make the non-words represent plausible foils for Māori lexical items.About 10,000 non-words  were generated by using a Māori dictionary  and two In the experiment, all participants are presented with the same set of 341 stimuli, each time in a different random order. They are provided with an instruction that the list of stimuli contains some Māori words (or phrases) and some Māori-like non-words (or phrases). Stimuli are presented orthographically, rather than auditorily, in order to remove the influence of phonetic factors such as how confident the speaker sounds. When participants are able to map between orthographic and phonological forms, written stimuli can activate phonological knowledge above and beyond orthographic knowledge . We assuIn Experiment 1, participants see a stimulus in the middle of the screen, which can be either a real word , or a Māori-like non-word (or a phrase of two non-words). Participants are asked to rate how confident they are that each item is an actual Māori word or phrase, using a five-point rating scale ranging from 1 to 5. To clarify the rating scale, the following additional descriptions were added next to the endpoints of the scale respectively–the lowest rating ‘1’ means that participants are ‘Confident that it is NOT a Māori word’ and the highest rating ‘5’ indicates that participants are ‘Confident that it IS a Māori word’. After finishing the rating task, participants are asked to complete a post-questionnaire which includes 19 demographic questions.The results of the Māori word identification task reveal that, consistent with Oh et al.  non-Māorako, hope, ihu, kaiwhakahaere, karu, mate, pango, pokohiwi, turituri, upoko, waha) and the rest of them are rated above 4. The word with the lowest average rating (3.25) is hope (‘waist’) which also exists as a word in English.In We fit a (logit) ordinal mixed-effects regression model, considering the predictors of interest–word type  and phonotactic score–and including control factors of number of neighbours, stimulus length and the presence of macron (treatment coded with absent as the reference level). The model is fit in a stepwise procedure, described in detail in the supplementary materials. The initial fitting procedure focuses on fixed effects only, and later stages add intercepts and slopes. The process considers all two-way interactions between a predictor of interest and a control predictor, as well as the interaction between the two primary predictors of interest.The summary of the selected ordinal mixed-effects regression model is given in Finally, the model reveals a significant interaction between the phonotactic score and word versus non-word stimuli, as suggested by We also note that the rating of the words sits above 4 on average, and at or close to 5 for many words–that is, participants have a high level of confidence that these are real words. This contrasts with the highest frequency bin for Panther et al. , where tGiven that phonotactic sensitivity appears to not be relevant for words that are well-known, we asked whether the patterns we see hold true over the whole data set, or whether they are different for participants with different degrees of apparent knowledge of Māori.how much Māori word knowledge participants have, with participants with higher values of d-prime having more knowledge. In this analysis, we are interested in whether participants with different degrees of word knowledge show different degrees of sensitivity to phonotactic score, presence of a macron, and number of neighbours. Thus, we build on the previous ordinal regression model to allow every term to interact with d-prime, and we investigate the nature of these interactions. The resulting model is shown in To measure a participant’s apparent knowledge of Māori, we use d-prime  which mespecific and explicit knowledge in this task: rather than using presence of a macron as a proxy, or a sort of generalised “Māori-like-ness” offered by phonotactic score, they make their ratings based on confident awareness of the existence of a word where possible, and based in part on awareness of specific highly-similar words .The partial effect plots for the interactions are given in Experiment 1 has shown that non-Māori-speaking New Zealanders were, on the whole, very accurate at discriminating the real Māori words in our experiment from the non-real foils. There were 121 words that were identified, on average, with a high degree of confidence (average rating >4). Significant effects of phonotactic and neighbourhood cues on non-Māori-speaking New Zealanders’ ratings also reveal that they possess sophisticated proto-lexical knowledge which makes them more likely to accept highly Māori-like non-words than less Māori-like non-words. Moreover, increased word-knowledge led to changes in the use of other linguistic cues for this task, in two ways. First, for the set of real words in this experiment, which are in general very familiar, we do not see any phonotactic effect. And second, when we look across individual participants, those with greater discrimination between words and non-words are relying less on the visual cue of the macron, and more on lexical neighbourhood effects–which require some degree of lexical or proto-lexical knowledge.Given the results from Oh et al.  and PantAotearoa, Māori, and Pākehā were also removed, because providing definitions for these highly-frequent nouns may not be trivial–there are no straightforward English translation equivalents. For the second experiment, which tests participants’ active lexical knowledge of Māori, the list of stimuli therefore consists of 118 words  to 5 (‘Very confident’). During the first part, the identical set of 118 stimuli are presented in a different random order to each participant. After finishing the definition task, participants complete a post-questionnaire which is the same as the one used in Experiment 1. The accuracy of the definitions in Experiment 2 were hand-coded by a fluent speaker of Māori.mauī (‘left’) and 99% for kia ora (‘hello’). For each word, the rate of getting correct responses differs substantially, ranging from 4% for The range across respondents was large–ranging from 8 to 116 correct items, with a mean of 71, a median of 72, and a standard deviation 24. Viewed by participant, then, we also reach the conclusion that the average New Zealander can define about 70 words, although there is considerable variability across participants. This, then, provides an answer to our first research question, regarding an approximate estimation of the active Māori vocabulary of New Zealanders.define words. We included all the fixed effects from Experiment 1 , and added a predictor of familiarity–the predicted mean rating for the word from Experiment 1, added in a manner that controls for nonlinearities and clustering , we have shown that the semantic lexicon is much smaller than the number of word-forms that can be passively identified as being Māori words. While results from participants varied, there were 70 words in our experiment that could be accurately defined by more than half of our participants. This confirms previous reports that the active lexicon is not large and does so using a methodology that explicitly probes the ability to actively produce a correct definition. However, the number of words accurately defined is significantly smaller than the number of words which can be recognised as Māori word-forms. This highlights the importance of the task used, when assessing the size of the vocabulary. Assessing changes in the Māori vocabulary size over the last few decades is currently difficult, due to the different methodologies used, all of which have tapped into different degrees of relatively passive knowledge.This result provides strong empirical support to the claims by Oh et al.  and PantIn terms of our second research question, regarding phonotactics, our results in the discrimination task reinforce the conclusions of Panther et al. , that phFurther evidence that explicit knowledge can reduce reliance on phonotactics comes from the analysis of individual variation, which shows that the effects of phonotactics and of having a macron also lessen in participants with high levels of overt knowledge. Conversely, these participants presumably have stronger lexical knowledge, and thus the neighbourhood density effect strengthens.The interpretation that factors such as phonotactics can become less important with overt knowledge also supports the results of the definition task, where we find no significant predictors except for accuracy in the discrimination task. Words that were more accurately identified as real in the discrimination task were also better defined. There was no further effect of phonotactics, macron, or neighbourhood density. Participants either know the word, and can define it, or they do not. These linguistic factors appear to have had no additional direct role in the likelihood of acquiring the word meaning once a word becomes readily recognisable; however, such factors do still play an indirect role, in determining the recognisability of a word in the first place.We know that phonotactic effects have been found in classic results on the effect of phonotactic probability in lexical decision in English (e.g. ). AnalysIn this context, it may seem superficially surprising that our discrimination task does not show any strong effect of phonotactics for real words. However a crucial difference between our task and a classical lexical decision task is that our task is not speeded. Errors in lexical decision tasks with native speakers come from errors induced by speed, rather than from absence of relevant lexical knowledge, whereas errors in our unspeeded task come from not recognising the word. Indeed, Vitevitch and Luce assume that the speeded nature of the task as key to interpreting their results: “a response may be initiated when activation for a unique lexical item has reached some criterion or threshold. However, when a single lexical item fails to receive sufficient activation within the time period required for a response, decisions may be based on the overall level of lexically based activity in the recognition system  (p.387)”Our task also differs from classic lexical decision in other ways, including the use of a rating scale, making a direct comparison between our results and work on English impossible. What our results do highlight is that more work is required to fully disentangle the role of phonotactic knowledge across different tasks and across different linguistic contexts.Finally, these results support the conclusions of Oh et al.  and PantWe have conducted experiments on New Zealanders who do not speak Māori, in order to assess the size of their active lexicon of the Māori Language. We identify 70 words that can be defined at above-average rates by this population.This result plays an important role in providing an empirical basis for claims in the wider literature that non-Māori speakers in New Zealand possess a large ‘proto-lexicon’ of words and word-parts that they cannot define and are not actively aware of. We have confirmed that, despite results showing that this population seems to ‘know’ a very large number of words, the size of their active lexicon–words that can be actively defined–is very limited. The contribution of linguistic factors that seem to require a lexicon, such as phonotactics and neighbourhood density, support the claims that the implicit knowledge is much more extensive than this small active vocabulary would suggest.Phonotactic well-formedness affects the likelihood that a non-word will be mistakenly recognised as a word, but not the success rate for high-frequency real words, nor the likelihood of successful definition of these words. Indeed, a variety of results point to such linguistic factors playing a role in tasks and participants involving low certainty, but not in tasks for which participants can call on active and explicit knowledge.In general, these results confirm the need for precision in our definitions when investigating vocabulary size, and the factors that may influence this. Many scholars have highlighted the fact that what can be described as “word knowledge” exists on a continuum –44. For S1 File(HTML)Click here for additional data file.