How food retailing changed in Turkey: spread of self-service technologies

Abstract

Purpose The purpose of this paper is to predict customers’ intentions to use self-checkouts based on the technology acceptance model (TAM). Design/methodology/approach The questionnaire consisted of constructs taken from the existing literature such as perceived ease of use (PEU), perceived usefulness (PU), behavioral intentions, technology anxiety (TA), perceived risk (PR), need for interaction (NI), and situational factors (SF). Before preparing the questionnaire, the focus group studies were organized to gain deeper insights regarding customers’ views about self-checkouts. Based on the results of the focus groups, some items in the constructs were adapted, and the questionnaire was generated. The field study was conducted via face-to-face survey with 500 customers chosen by stratified random sampling. Confirmatory factor analysis and structural equation modeling were used to validate the relationships hypothesized in the conceptual model among constructs. Findings Out of the 16 hypotheses, 10 were found to be significant. The hypotheses related to the effects of PR, PEU, PU, intentions, while the effects of NI on PU and intentions; the effects of SF on intentions were not accepted in the study. According to the findings, PEU, PU, and TA affected intentions whereas PR, NI, and SF did not. Research limitations/implications There were some limitations related to demographics, attitudes, SF, and actual usage of self-checkouts. Practical implications To avoid queues, retailers should install a sufficient number of user-friendly and simple interfaced self-checkouts with well-trained employees to encourage usage and reduce the perceived risk and anxiety. Originality/value The main contribution of this study was that the effects of different constructs were measured on Turkish customers’ intentions to use self-checkouts, which could be used in formulating marketing strategies as well as considering future research directions. The paper also provided additional insights into the effects of SF, TA, NI, and PR, all of which were added to the TAM in this study.

Analysing flow experience on repurchase intention in e-retailing during COVID-19

“…According to TUIK (2021), İzmir is Turkey's third biggest city by population with 4.394 million. Hence, the sample size was found to be n = 384 at 95% (standard deviation of the scale as 0.5; t = 1.96) confidence interval with a margin of error of 5% ( n 0 =(1.96 2 × 0.5 2 )/(0.05 2 ) = 384) (Barlett et al , 2001, p. 47; Sekaran, 2003; Kazancoglu and Yarimoglu, 2018, p. 9). Cochran's (1977) correction formula was used to calculate the final sample size ( n = n 0 /(1+ n 0 /Population) (Barlett et al , 2001, p. 46).…”

“…Cochran's (1977) correction formula was used to calculate the final sample size ( n = n 0 /(1+ n 0 /Population) (Barlett et al , 2001, p. 46). Whereas assuming a 65% response rate, a minimum sample size of 590 should be used (Barlett et al , 2001, p. 46; Kazancoglu and Yarimoglu, 2018, p. 9). By the way, 152 participants who answered the online questionnaire do not shop from X Online Market, and these consumers were excluded from the analysis.…”

Brick-and-mortar retailers: Becoming smarter with innovative technologies

“…In that sense, Wang, Harris, & Patterson (2012) highlight the importance of preventing frequent failures and providing speedy recovery in the SST context. Therefore, retailers should install a sufficient number of user-friendly self-checkouts with attentive employees to encourage usage and reduce the perceived risk and anxiety (Kazancoglu & Yarimoglu, 2018).…”

What drives the purchasing of foods with high sugar? Evidence from Turkey

“…Findings from developing countries suggest that the rapid spread and increasing preference of modern supermarkets lead to higher consumption of highly processed, energy-dense products (Demmler et al, 2018;Rischke et al, 2015). The Turkish food retailing market has a highly fragmented structure (Kazancoglu and Kursunluoglu Yarimoglu, 2018). Organised retailers such as supermarkets and especially discounter chains are on the rise.…”

“…Instead of focussing on single category purchases or subsets of unhealthful categories, this study employs shopping basket data to allow for a complete recording of the household's food purchases. In Turkey, the retail environment is undergoing a constant modernisation, moving away from open bazaars and traditional markets to national grocery chains (Kazancoglu and Kursunluoglu Yarimoglu, 2018) which can impact the healthfulness of food purchases (Volpe et al, 2013;Rudi and Çakır, 2017;Pechey and Monsivais, 2015). Consequently, this large-scale study provides valuable insights to support health professionals and policymakers in improving public health.…”